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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 17 new columns ({'call-number', 'issued', 'original-date', 'event-place', 'collection-title', 'author', 'publisher-place', 'page', 'number-of-pages', 'container-title', 'collection-number', 'keyword', 'accessed', 'number', 'URL', 'ISBN', 'DOI'}) and 9 missing columns ({'doi', 'address', 'booktitle', 'year', 'citation-name', 'raw_title', 'url', 'pages', 'month'}).
This happened while the json dataset builder was generating data using
hf://datasets/huashen218/convxai-cia-dataset/CHI_papers.json (at revision 51984dd3e03d28441c5a87213f6606489d2c8878)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
abstract: string
publisher: string
keyword: string
id: string
accessed: struct<date-parts: list<item: list<item: int64>>>
child 0, date-parts: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
DOI: string
type: string
number-of-pages: string
number: string
ISBN: string
title: string
call-number: string
issued: struct<date-parts: list<item: list<item: int64>>>
child 0, date-parts: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
collection-title: string
author: list<item: struct<family: string, given: string>>
child 0, item: struct<family: string, given: string>
child 0, family: string
child 1, given: string
publisher-place: string
container-title: string
collection-number: string
URL: string
original-date: struct<date-parts: list<item: list<item: int64>>>
child 0, date-parts: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
event-place: string
page: string
to
{'abstract': Value(dtype='string', id=None), 'doi': Value(dtype='string', id=None), 'address': Value(dtype='string', id=None), 'booktitle': Value(dtype='string', id=None), 'year': Value(dtype='string', id=None), 'type': Value(dtype='string', id=None), 'citation-name': Value(dtype='string', id=None), 'raw_title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'pages': Value(dtype='string', id=None), 'month': Value(dtype='string', id=None), 'publisher': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 17 new columns ({'call-number', 'issued', 'original-date', 'event-place', 'collection-title', 'author', 'publisher-place', 'page', 'number-of-pages', 'container-title', 'collection-number', 'keyword', 'accessed', 'number', 'URL', 'ISBN', 'DOI'}) and 9 missing columns ({'doi', 'address', 'booktitle', 'year', 'citation-name', 'raw_title', 'url', 'pages', 'month'}).
This happened while the json dataset builder was generating data using
hf://datasets/huashen218/convxai-cia-dataset/CHI_papers.json (at revision 51984dd3e03d28441c5a87213f6606489d2c8878)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
booktitle string | publisher string | month string | id string | citation-name string | title string | address string | doi string | url string | year string | abstract string | raw_title string | type string | pages string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.1 | modarressi-etal-2022-adapler | AdapLeR: Speeding up Inference by Adaptive Length Reduction | Dublin, Ireland | 10.18653/v1/2022.acl-long.1 | https://aclanthology.org/2022.acl-long.1 | 2022 | Pre-trained language models have shown stellar performance in various downstream tasks. But, this usually comes at the cost of high latency and computation, hindering their usage in resource-limited settings. In this work, we propose a novel approach for reducing the computational cost of BERT with minimal loss in down... | {A}dap{L}e{R}: Speeding up Inference by Adaptive Length Reduction | inproceedings | 1--15 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.2 | belz-etal-2022-quantified | Quantified Reproducibility Assessment of NLP Results | Dublin, Ireland | 10.18653/v1/2022.acl-long.2 | https://aclanthology.org/2022.acl-long.2 | 2022 | This paper describes and tests a method for carrying out quantified reproducibility assessment (QRA) that is based on concepts and definitions from metrology. QRA produces a single score estimating the degree of reproducibility of a given system and evaluation measure, on the basis of the scores from, and differences b... | Quantified Reproducibility Assessment of {NLP} Results | inproceedings | 16--28 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.3 | yu-etal-2022-rare | Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings | Dublin, Ireland | 10.18653/v1/2022.acl-long.3 | https://aclanthology.org/2022.acl-long.3 | 2022 | Recent studies have determined that the learned token embeddings of large-scale neural language models are degenerated to be anisotropic with a narrow-cone shape. This phenomenon, called the representation degeneration problem, facilitates an increase in the overall similarity between token embeddings that negatively a... | Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings | inproceedings | 29--45 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.4 | seker-etal-2022-alephbert | AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level | Dublin, Ireland | 10.18653/v1/2022.acl-long.4 | https://aclanthology.org/2022.acl-long.4 | 2022 | Large Pre-trained Language Models (PLMs) have become ubiquitous in the development of language understanding technology and lie at the heart of many artificial intelligence advances. While advances reported for English using PLMs are unprecedented, reported advances using PLMs for Hebrew are few and far between. The pr... | {A}leph{BERT}: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level | inproceedings | 46--56 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.5 | li-etal-2022-learning | Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning | Dublin, Ireland | 10.18653/v1/2022.acl-long.5 | https://aclanthology.org/2022.acl-long.5 | 2022 | Neural discrete reasoning (NDR) has shown remarkable progress in combining deep models with discrete reasoning. However, we find that existing NDR solution suffers from large performance drop on hypothetical questions, e.g. {``}what the annualized rate of return would be if the revenue in 2020 was doubled{''}. The key ... | Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning | inproceedings | 57--69 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.6 | zaharia-etal-2022-domain | Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification | Dublin, Ireland | 10.18653/v1/2022.acl-long.6 | https://aclanthology.org/2022.acl-long.6 | 2022 | Complex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages. As such, it becomes increasingly more difficult to develop a ... | Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification | inproceedings | 70--80 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.7 | liang-etal-2022-jointcl | JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection | Dublin, Ireland | 10.18653/v1/2022.acl-long.7 | https://aclanthology.org/2022.acl-long.7 | 2022 | Zero-shot stance detection (ZSSD) aims to detect the stance for an unseen target during the inference stage. In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning. Specifically, a stance contrast... | {J}oint{CL}: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection | inproceedings | 81--91 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.8 | ramachandran-etal-2022-caspi | [CASPI] Causal-aware Safe Policy Improvement for Task-oriented Dialogue | Dublin, Ireland | 10.18653/v1/2022.acl-long.8 | https://aclanthology.org/2022.acl-long.8 | 2022 | The recent success of reinforcement learning (RL) in solving complex tasks is often attributed to its capacity to explore and exploit an environment.Sample efficiency is usually not an issue for tasks with cheap simulators to sample data online.On the other hand, Task-oriented Dialogues (ToD) are usually learnt from of... | [{CASPI}] Causal-aware Safe Policy Improvement for Task-oriented Dialogue | inproceedings | 92--102 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.9 | ma-etal-2022-unitranser | UniTranSeR: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog System | Dublin, Ireland | 10.18653/v1/2022.acl-long.9 | https://aclanthology.org/2022.acl-long.9 | 2022 | As a more natural and intelligent interaction manner, multimodal task-oriented dialog system recently has received great attention and many remarkable progresses have been achieved. Nevertheless, almost all existing studies follow the pipeline to first learn intra-modal features separately and then conduct simple featu... | {U}ni{T}ran{S}e{R}: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog System | inproceedings | 103--114 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.10 | feng-etal-2022-dynamic | Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking | Dublin, Ireland | 10.18653/v1/2022.acl-long.10 | https://aclanthology.org/2022.acl-long.10 | 2022 | Dialogue State Tracking (DST) aims to keep track of users{'} intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under-studied problem. Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership... | Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking | inproceedings | 115--126 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.11 | zhang-etal-2022-attention | Attention Temperature Matters in Abstractive Summarization Distillation | Dublin, Ireland | 10.18653/v1/2022.acl-long.11 | https://aclanthology.org/2022.acl-long.11 | 2022 | Recent progress of abstractive text summarization largely relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive. This paper aims to distill these large models into smaller ones for faster inference and with minimal performance loss. Pseudo-labeling based methods are po... | Attention Temperature Matters in Abstractive Summarization Distillation | inproceedings | 127--141 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.12 | chen-etal-2022-towards | Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation | Dublin, Ireland | 10.18653/v1/2022.acl-long.12 | https://aclanthology.org/2022.acl-long.12 | 2022 | This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neural machine translation (NMT) model is tested on source languages unseen during supervised training. Following this idea, we present SixT+, ... | Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation | inproceedings | 142--157 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.13 | pan-etal-2022-topwords | TopWORDS-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain Chinese Texts via Bayesian Inference | Dublin, Ireland | 10.18653/v1/2022.acl-long.13 | https://aclanthology.org/2022.acl-long.13 | 2022 | Processing open-domain Chinese texts has been a critical bottleneck in computational linguistics for decades, partially because text segmentation and word discovery often entangle with each other in this challenging scenario. No existing methods yet can achieve effective text segmentation and word discovery simultaneou... | {T}op{WORDS}-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain {C}hinese Texts via {B}ayesian Inference | inproceedings | 158--169 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.14 | li-etal-2022-unsupervised-multiple | An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition | Dublin, Ireland | 10.18653/v1/2022.acl-long.14 | https://aclanthology.org/2022.acl-long.14 | 2022 | Cross-lingual named entity recognition task is one of the critical problems for evaluating the potential transfer learning techniques on low resource languages. Knowledge distillation using pre-trained multilingual language models between source and target languages have shown their superiority in transfer. However, ex... | An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition | inproceedings | 170--179 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.15 | moro-etal-2022-discriminative | Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature | Dublin, Ireland | 10.18653/v1/2022.acl-long.15 | https://aclanthology.org/2022.acl-long.15 | 2022 | Although current state-of-the-art Transformer-based solutions succeeded in a wide range for single-document NLP tasks, they still struggle to address multi-input tasks such as multi-document summarization. Many solutions truncate the inputs, thus ignoring potential summary-relevant contents, which is unacceptable in th... | Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature | inproceedings | 180--189 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.16 | huang-etal-2022-sparse | Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm | Dublin, Ireland | 10.18653/v1/2022.acl-long.16 | https://aclanthology.org/2022.acl-long.16 | 2022 | Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit. However, under the trending pretrain-and-finetune paradigm, we postulate a counter-traditional hypothesis, that is: pruning increases the risk of ove... | Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm | inproceedings | 190--200 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.17 | kambhatla-etal-2022-cipherdaug | CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation | Dublin, Ireland | 10.18653/v1/2022.acl-long.17 | https://aclanthology.org/2022.acl-long.17 | 2022 | We propose a novel data-augmentation technique for neural machine translation based on ROT-$k$ ciphertexts. ROT-$k$ is a simple letter substitution cipher that replaces a letter in the plaintext with the $k$th letter after it in the alphabet. We first generate multiple ROT-$k$ ciphertexts using different values of $k$ ... | {C}ipher{DA}ug: Ciphertext based Data Augmentation for Neural Machine Translation | inproceedings | 201--218 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.18 | patil-etal-2022-overlap | Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages | Dublin, Ireland | 10.18653/v1/2022.acl-long.18 | https://aclanthology.org/2022.acl-long.18 | 2022 | Pre-trained multilingual language models such as mBERT and XLM-R have demonstrated great potential for zero-shot cross-lingual transfer to low web-resource languages (LRL). However, due to limited model capacity, the large difference in the sizes of available monolingual corpora between high web-resource languages (HRL... | Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages | inproceedings | 219--233 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.19 | zhuang-etal-2022-long | Long-range Sequence Modeling with Predictable Sparse Attention | Dublin, Ireland | 10.18653/v1/2022.acl-long.19 | https://aclanthology.org/2022.acl-long.19 | 2022 | Self-attention mechanism has been shown to be an effective approach for capturing global context dependencies in sequence modeling, but it suffers from quadratic complexity in time and memory usage. Due to the sparsity of the attention matrix, much computation is redundant. Therefore, in this paper, we design an effici... | Long-range Sequence Modeling with Predictable Sparse Attention | inproceedings | 234--243 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.20 | geng-etal-2022-improving | Improving Personalized Explanation Generation through Visualization | Dublin, Ireland | 10.18653/v1/2022.acl-long.20 | https://aclanthology.org/2022.acl-long.20 | 2022 | In modern recommender systems, there are usually comments or reviews from users that justify their ratings for different items. Trained on such textual corpus, explainable recommendation models learn to discover user interests and generate personalized explanations. Though able to provide plausible explanations, existi... | Improving Personalized Explanation Generation through Visualization | inproceedings | 244--255 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.21 | zhang-etal-2022-new | New Intent Discovery with Pre-training and Contrastive Learning | Dublin, Ireland | 10.18653/v1/2022.acl-long.21 | https://aclanthology.org/2022.acl-long.21 | 2022 | New intent discovery aims to uncover novel intent categories from user utterances to expand the set of supported intent classes. It is a critical task for the development and service expansion of a practical dialogue system. Despite its importance, this problem remains under-explored in the literature. Existing approac... | New Intent Discovery with Pre-training and Contrastive Learning | inproceedings | 256--269 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.22 | davoodi-etal-2022-modeling | Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts | Dublin, Ireland | 10.18653/v1/2022.acl-long.22 | https://aclanthology.org/2022.acl-long.22 | 2022 | Decisions on state-level policies have a deep effect on many aspects of our everyday life, such as health-care and education access. However, there is little understanding of how these policies and decisions are being formed in the legislative process. We take a data-driven approach by decoding the impact of legislatio... | {M}odeling {U.S.} State-Level Policies by Extracting Winners and Losers from Legislative Texts | inproceedings | 270--284 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.23 | ma-etal-2022-structural | Structural Characterization for Dialogue Disentanglement | Dublin, Ireland | 10.18653/v1/2022.acl-long.23 | https://aclanthology.org/2022.acl-long.23 | 2022 | Tangled multi-party dialogue contexts lead to challenges for dialogue reading comprehension, where multiple dialogue threads flow simultaneously within a common dialogue record, increasing difficulties in understanding the dialogue history for both human and machine. Previous studies mainly focus on utterance encoding ... | Structural Characterization for Dialogue Disentanglement | inproceedings | 285--297 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.24 | zhu-etal-2022-multi | Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems | Dublin, Ireland | 10.18653/v1/2022.acl-long.24 | https://aclanthology.org/2022.acl-long.24 | 2022 | Empathetic dialogue assembles emotion understanding, feeling projection, and appropriate response generation. Existing work for empathetic dialogue generation concentrates on the two-party conversation scenario. Multi-party dialogues, however, are pervasive in reality. Furthermore, emotion and sensibility are typically... | Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems | inproceedings | 298--307 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.25 | tu-etal-2022-misc | MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation | Dublin, Ireland | 10.18653/v1/2022.acl-long.25 | https://aclanthology.org/2022.acl-long.25 | 2022 | Applying existing methods to emotional support conversation{---}which provides valuable assistance to people who are in need{---}has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user{'}s instant mental state; (b) most of them focus on expres... | {MISC}: A Mixed Strategy-Aware Model integrating {COMET} for Emotional Support Conversation | inproceedings | 308--319 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.26 | du-etal-2022-glm | GLM: General Language Model Pretraining with Autoregressive Blank Infilling | Dublin, Ireland | 10.18653/v1/2022.acl-long.26 | https://aclanthology.org/2022.acl-long.26 | 2022 | There have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5). However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (... | {GLM}: General Language Model Pretraining with Autoregressive Blank Infilling | inproceedings | 320--335 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.27 | qi-etal-2022-quoter | QuoteR: A Benchmark of Quote Recommendation for Writing | Dublin, Ireland | 10.18653/v1/2022.acl-long.27 | https://aclanthology.org/2022.acl-long.27 | 2022 | It is very common to use quotations (quotes) to make our writings more elegant or convincing. To help people find appropriate quotes efficiently, the task of quote recommendation is presented, aiming to recommend quotes that fit the current context of writing. There have been various quote recommendation approaches, bu... | {Q}uote{R}: A Benchmark of Quote Recommendation for Writing | inproceedings | 336--348 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.28 | gao-etal-2022-towards | Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification | Dublin, Ireland | 10.18653/v1/2022.acl-long.28 | https://aclanthology.org/2022.acl-long.28 | 2022 | Predicting the approval chance of a patent application is a challenging problem involving multiple facets. The most crucial facet is arguably the novelty {---} \textit{35 U.S. Code {\S} 102} rejects more recent applications that have very similar prior arts. Such novelty evaluations differ the patent approval predictio... | Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification | inproceedings | 349--372 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.29 | heo-etal-2022-hypergraph | Hypergraph Transformer: Weakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering | Dublin, Ireland | 10.18653/v1/2022.acl-long.29 | https://aclanthology.org/2022.acl-long.29 | 2022 | Knowledge-based visual question answering (QA) aims to answer a question which requires visually-grounded external knowledge beyond image content itself. Answering complex questions that require multi-hop reasoning under weak supervision is considered as a challenging problem since i) no supervision is given to the rea... | Hypergraph {T}ransformer: {W}eakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering | inproceedings | 373--390 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.30 | li-etal-2022-cross-utterance | Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech | Dublin, Ireland | 10.18653/v1/2022.acl-long.30 | https://aclanthology.org/2022.acl-long.30 | 2022 | Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems. In this paper, a cross-utterance conditional VAE (CUC-VAE) is proposed to estimate a posterior probability distribution of the latent prosody features for each phoneme by conditioning on ac... | Cross-Utterance Conditioned {VAE} for Non-Autoregressive Text-to-Speech | inproceedings | 391--400 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.31 | mireshghallah-etal-2022-mix | Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models | Dublin, Ireland | 10.18653/v1/2022.acl-long.31 | https://aclanthology.org/2022.acl-long.31 | 2022 | Recent work on controlled text generation has either required attribute-based fine-tuning of the base language model (LM), or has restricted the parameterization of the attribute discriminator to be compatible with the base autoregressive LM. In this work, we propose Mix and Match LM, a global score-based alternative f... | Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models | inproceedings | 401--415 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.32 | ramesh-kashyap-etal-2022-different | So Different Yet So Alike! Constrained Unsupervised Text Style Transfer | Dublin, Ireland | 10.18653/v1/2022.acl-long.32 | https://aclanthology.org/2022.acl-long.32 | 2022 | Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content while adapting to the target domain. However, it does not explicitly maintain other attributes between the source and translated text: e.g., text length and descriptiveness. Maintaining con... | So Different Yet So Alike! Constrained Unsupervised Text Style Transfer | inproceedings | 416--431 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.33 | du-etal-2022-e | e-CARE: a New Dataset for Exploring Explainable Causal Reasoning | Dublin, Ireland | 10.18653/v1/2022.acl-long.33 | https://aclanthology.org/2022.acl-long.33 | 2022 | Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal fact to facilitate the causal reasoning process. However, such explanation information still remain... | e-{CARE}: a New Dataset for Exploring Explainable Causal Reasoning | inproceedings | 432--446 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.34 | xu-etal-2022-fantastic | Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension | Dublin, Ireland | 10.18653/v1/2022.acl-long.34 | https://aclanthology.org/2022.acl-long.34 | 2022 | Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained readin... | Fantastic Questions and Where to Find Them: {F}airytale{QA} {--} An Authentic Dataset for Narrative Comprehension | inproceedings | 447--460 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.35 | li-xiong-2022-kafsp | KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base | Dublin, Ireland | 10.18653/v1/2022.acl-long.35 | https://aclanthology.org/2022.acl-long.35 | 2022 | In this paper, we study two issues of semantic parsing approaches to conversational question answering over a large-scale knowledge base: (1) The actions defined in grammar are not sufficient to handle uncertain reasoning common in real-world scenarios. (2) Knowledge base information is not well exploited and incorpora... | {K}a{FSP}: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base | inproceedings | 461--473 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.36 | huang-etal-2022-multilingual | Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment | Dublin, Ireland | 10.18653/v1/2022.acl-long.36 | https://aclanthology.org/2022.acl-long.36 | 2022 | Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore multilingual KG completion, which leverages limited seed alignment as... | Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment | inproceedings | 474--485 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.37 | guo-etal-2022-modeling | Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization | Dublin, Ireland | 10.18653/v1/2022.acl-long.37 | https://aclanthology.org/2022.acl-long.37 | 2022 | Automatic code summarization, which aims to describe the source code in natural language, has become an essential task in software maintenance. Our fellow researchers have attempted to achieve such a purpose through various machine learning-based approaches. One key challenge keeping these approaches from being practic... | Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization | inproceedings | 486--500 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.38 | zheng-etal-2022-fewnlu | FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding | Dublin, Ireland | 10.18653/v1/2022.acl-long.38 | https://aclanthology.org/2022.acl-long.38 | 2022 | The few-shot natural language understanding (NLU) task has attracted much recent attention. However, prior methods have been evaluated under a disparate set of protocols, which hinders fair comparison and measuring the progress of the field. To address this issue, we introduce an evaluation framework that improves prev... | {F}ew{NLU}: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding | inproceedings | 501--516 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.39 | zhang-etal-2022-learn | Learn to Adapt for Generalized Zero-Shot Text Classification | Dublin, Ireland | 10.18653/v1/2022.acl-long.39 | https://aclanthology.org/2022.acl-long.39 | 2022 | Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes, and the parameters keep stationar... | Learn to Adapt for Generalized Zero-Shot Text Classification | inproceedings | 517--527 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.40 | yang-etal-2022-tableformer | TableFormer: Robust Transformer Modeling for Table-Text Encoding | Dublin, Ireland | 10.18653/v1/2022.acl-long.40 | https://aclanthology.org/2022.acl-long.40 | 2022 | Understanding tables is an important aspect of natural language understanding. Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias. Such spurious biases make the model vulnerable to row and column order perturbations. Additionall... | {T}able{F}ormer: Robust Transformer Modeling for Table-Text Encoding | inproceedings | 528--537 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.41 | xu-etal-2022-perceiving | Perceiving the World: Question-guided Reinforcement Learning for Text-based Games | Dublin, Ireland | 10.18653/v1/2022.acl-long.41 | https://aclanthology.org/2022.acl-long.41 | 2022 | Text-based games provide an interactive way to study natural language processing. While deep reinforcement learning has shown effectiveness in developing the game playing agent, the low sample efficiency and the large action space remain to be the two major challenges that hinder the DRL from being applied in the real ... | Perceiving the World: Question-guided Reinforcement Learning for Text-based Games | inproceedings | 538--560 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.42 | jia-etal-2022-neural | Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization | Dublin, Ireland | 10.18653/v1/2022.acl-long.42 | https://aclanthology.org/2022.acl-long.42 | 2022 | In zero-shot multilingual extractive text summarization, a model is typically trained on English summarization dataset and then applied on summarization datasets of other languages. Given English gold summaries and documents, sentence-level labels for extractive summarization are usually generated using heuristics. How... | Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization | inproceedings | 561--570 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.43 | wang-etal-2022-shot | Few-Shot Class-Incremental Learning for Named Entity Recognition | Dublin, Ireland | 10.18653/v1/2022.acl-long.43 | https://aclanthology.org/2022.acl-long.43 | 2022 | Previous work of class-incremental learning for Named Entity Recognition (NER) relies on the assumption that there exists abundance of labeled data for the training of new classes. In this work, we study a more challenging but practical problem, \textit{i.e.}, few-shot class-incremental learning for NER, where an NER m... | Few-Shot Class-Incremental Learning for Named Entity Recognition | inproceedings | 571--582 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.44 | zhao-etal-2022-improving | Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation | Dublin, Ireland | 10.18653/v1/2022.acl-long.44 | https://aclanthology.org/2022.acl-long.44 | 2022 | Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available. Optimization-based meta-learning algorithms achieve promising results in low-resource scenarios by adapting a well-generalized model initialization to handle new tasks. Nonetheless, these appr... | Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation | inproceedings | 583--595 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.45 | bandel-etal-2022-quality | Quality Controlled Paraphrase Generation | Dublin, Ireland | 10.18653/v1/2022.acl-long.45 | https://aclanthology.org/2022.acl-long.45 | 2022 | Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, the original sentence. Generating high-quality paraphrases is challenging as it becomes increasingly hard to pr... | Quality Controlled Paraphrase Generation | inproceedings | 596--609 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.46 | he-yiu-2022-controllable | Controllable Dictionary Example Generation: Generating Example Sentences for Specific Targeted Audiences | Dublin, Ireland | 10.18653/v1/2022.acl-long.46 | https://aclanthology.org/2022.acl-long.46 | 2022 | Example sentences for targeted words in a dictionary play an important role to help readers understand the usage of words. Traditionally, example sentences in a dictionary are usually created by linguistics experts, which are labor-intensive and knowledge-intensive. In this paper, we introduce the problem of dictionary... | Controllable Dictionary Example Generation: Generating Example Sentences for Specific Targeted Audiences | inproceedings | 610--627 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.47 | nagoudi-etal-2022-arat5 | AraT5: Text-to-Text Transformers for Arabic Language Generation | Dublin, Ireland | 10.18653/v1/2022.acl-long.47 | https://aclanthology.org/2022.acl-long.47 | 2022 | Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-En... | {A}ra{T}5: Text-to-Text Transformers for {A}rabic Language Generation | inproceedings | 628--647 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.48 | feng-etal-2022-legal | Legal Judgment Prediction via Event Extraction with Constraints | Dublin, Ireland | 10.18653/v1/2022.acl-long.48 | https://aclanthology.org/2022.acl-long.48 | 2022 | While significant progress has been made on the task of Legal Judgment Prediction (LJP) in recent years, the incorrect predictions made by SOTA LJP models can be attributed in part to their failure to (1) locate the key event information that determines the judgment, and (2) exploit the cross-task consistency constrain... | Legal Judgment Prediction via Event Extraction with Constraints | inproceedings | 648--664 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.49 | kumar-2022-answer | Answer-level Calibration for Free-form Multiple Choice Question Answering | Dublin, Ireland | 10.18653/v1/2022.acl-long.49 | https://aclanthology.org/2022.acl-long.49 | 2022 | Pre-trained language models have recently shown that training on large corpora using the language modeling objective enables few-shot and zero-shot capabilities on a variety of NLP tasks, including commonsense reasoning tasks. This is achieved using text interactions with the model, usually by posing the task as a natu... | Answer-level Calibration for Free-form Multiple Choice Question Answering | inproceedings | 665--679 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.50 | dong-etal-2022-learning | Learning When to Translate for Streaming Speech | Dublin, Ireland | 10.18653/v1/2022.acl-long.50 | https://aclanthology.org/2022.acl-long.50 | 2022 | How to find proper moments to generate partial sentence translation given a streaming speech input? Existing approaches waiting-and-translating for a fixed duration often break the acoustic units in speech, since the boundaries between acoustic units in speech are not even. In this paper, we propose MoSST, a simple yet... | Learning When to Translate for Streaming Speech | inproceedings | 680--694 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.51 | yang-etal-2022-compact | Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking | Dublin, Ireland | 10.18653/v1/2022.acl-long.51 | https://aclanthology.org/2022.acl-long.51 | 2022 | Transformer based re-ranking models can achieve high search relevance through context- aware soft matching of query tokens with document tokens. To alleviate runtime complexity of such inference, previous work has adopted a late interaction architecture with pre-computed contextual token representations at the cost of ... | Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking | inproceedings | 695--707 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.52 | choi-etal-2022-early | Early Stopping Based on Unlabeled Samples in Text Classification | Dublin, Ireland | 10.18653/v1/2022.acl-long.52 | https://aclanthology.org/2022.acl-long.52 | 2022 | Early stopping, which is widely used to prevent overfitting, is generally based on a separate validation set. However, in low resource settings, validation-based stopping can be risky because a small validation set may not be sufficiently representative, and the reduction in the number of samples by validation split ma... | Early Stopping Based on Unlabeled Samples in Text Classification | inproceedings | 708--718 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.53 | chen-etal-2022-meta | Meta-learning via Language Model In-context Tuning | Dublin, Ireland | 10.18653/v1/2022.acl-long.53 | https://aclanthology.org/2022.acl-long.53 | 2022 | The goal of meta-learning is to learn to adapt to a new task with only a few labeled examples. Inspired by the recent progress in large language models, we propose $\textit{in-context tuning}$ (ICT), which recasts task adaptation and prediction as a simple sequence prediction problem: to form the input sequence, we con... | Meta-learning via Language Model In-context Tuning | inproceedings | 719--730 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.54 | yao-etal-2022-ais | It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books | Dublin, Ireland | 10.18653/v1/2022.acl-long.54 | https://aclanthology.org/2022.acl-long.54 | 2022 | Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers often need to decide what questions they should ask, in order to help students to improve their narrative understanding capabilities. We design an automated question-answer gener... | It is {AI}{'}s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children{'}s Story Books | inproceedings | 731--744 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.55 | zhang-etal-2022-prompt | Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning | Dublin, Ireland | 10.18653/v1/2022.acl-long.55 | https://aclanthology.org/2022.acl-long.55 | 2022 | Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult. We study interactive weakly-supervised learning{---}the problem of iteratively and automatically discovering novel... | Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning | inproceedings | 745--758 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.56 | kobayashi-etal-2022-constrained | Constrained Multi-Task Learning for Bridging Resolution | Dublin, Ireland | 10.18653/v1/2022.acl-long.56 | https://aclanthology.org/2022.acl-long.56 | 2022 | We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-tra... | Constrained Multi-Task Learning for Bridging Resolution | inproceedings | 759--770 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.57 | ghazarian-etal-2022-deam | DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations | Dublin, Ireland | 10.18653/v1/2022.acl-long.57 | https://aclanthology.org/2022.acl-long.57 | 2022 | Automatic evaluation metrics are essential for the rapid development of open-domain dialogue systems as they facilitate hyper-parameter tuning and comparison between models. Although recently proposed trainable conversation-level metrics have shown encouraging results, the quality of the metrics is strongly dependent o... | {DEAM}: Dialogue Coherence Evaluation using {AMR}-based Semantic Manipulations | inproceedings | 771--785 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.58 | cao-wang-2022-hibrids | HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization | Dublin, Ireland | 10.18653/v1/2022.acl-long.58 | https://aclanthology.org/2022.acl-long.58 | 2022 | Document structure is critical for efficient information consumption. However, it is challenging to encode it efficiently into the modern Transformer architecture. In this work, we present HIBRIDS, which injects Hierarchical Biases foR Incorporating Document Structure into attention score calculation. We further presen... | {HIBRIDS}: Attention with Hierarchical Biases for Structure-aware Long Document Summarization | inproceedings | 786--807 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.59 | zhang-etal-2022-de | De-Bias for Generative Extraction in Unified NER Task | Dublin, Ireland | 10.18653/v1/2022.acl-long.59 | https://aclanthology.org/2022.acl-long.59 | 2022 | Named entity recognition (NER) is a fundamental task to recognize specific types of entities from a given sentence. Depending on how the entities appear in the sentence, it can be divided into three subtasks, namely, Flat NER, Nested NER, and Discontinuous NER. Among the existing approaches, only the generative model c... | De-Bias for Generative Extraction in Unified {NER} Task | inproceedings | 808--818 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.60 | sorensen-etal-2022-information | An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels | Dublin, Ireland | 10.18653/v1/2022.acl-long.60 | https://aclanthology.org/2022.acl-long.60 | 2022 | Pre-trained language models derive substantial linguistic and factual knowledge from the massive corpora on which they are trained, and prompt engineering seeks to align these models to specific tasks. Unfortunately, existing prompt engineering methods require significant amounts of labeled data, access to model parame... | An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels | inproceedings | 819--862 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.61 | wang-etal-2022-expanding | Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation | Dublin, Ireland | 10.18653/v1/2022.acl-long.61 | https://aclanthology.org/2022.acl-long.61 | 2022 | The performance of multilingual pretrained models is highly dependent on the availability of monolingual or parallel text present in a target language. Thus, the majority of the world{'}s languages cannot benefit from recent progress in NLP as they have no or limited textual data. To expand possibilities of using NLP t... | Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation | inproceedings | 863--877 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.62 | feng-etal-2022-language | Language-agnostic BERT Sentence Embedding | Dublin, Ireland | 10.18653/v1/2022.acl-long.62 | https://aclanthology.org/2022.acl-long.62 | 2022 | While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embeddings by combining the best met... | Language-agnostic {BERT} Sentence Embedding | inproceedings | 878--891 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.63 | wan-etal-2022-nested | Nested Named Entity Recognition with Span-level Graphs | Dublin, Ireland | 10.18653/v1/2022.acl-long.63 | https://aclanthology.org/2022.acl-long.63 | 2022 | Span-based methods with the neural networks backbone have great potential for the nested named entity recognition (NER) problem. However, they face problems such as degenerating when positive instances and negative instances largely overlap. Besides, the generalization ability matters a lot in nested NER, as a large pr... | Nested Named Entity Recognition with Span-level Graphs | inproceedings | 892--903 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.64 | luo-etal-2022-cogtaskonomy | CogTaskonomy: Cognitively Inspired Task Taxonomy Is Beneficial to Transfer Learning in NLP | Dublin, Ireland | 10.18653/v1/2022.acl-long.64 | https://aclanthology.org/2022.acl-long.64 | 2022 | Is there a principle to guide transfer learning across tasks in natural language processing (NLP)? Taxonomy (Zamir et al., 2018) finds that a structure exists among visual tasks, as a principle underlying transfer learning for them. In this paper, we propose a cognitively inspired framework, CogTaskonomy, to learn taxo... | {C}og{T}askonomy: Cognitively Inspired Task Taxonomy Is Beneficial to Transfer Learning in {NLP} | inproceedings | 904--920 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.65 | su-etal-2022-rocbert | RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining | Dublin, Ireland | 10.18653/v1/2022.acl-long.65 | https://aclanthology.org/2022.acl-long.65 | 2022 | Large-scale pretrained language models have achieved SOTA results on NLP tasks. However, they have been shown vulnerable to adversarial attacks especially for logographic languages like Chinese. In this work, we propose RoCBert: a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word ... | {R}o{CB}ert: Robust {C}hinese Bert with Multimodal Contrastive Pretraining | inproceedings | 921--931 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.66 | dong-etal-2022-premise | Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues | Dublin, Ireland | 10.18653/v1/2022.acl-long.66 | https://aclanthology.org/2022.acl-long.66 | 2022 | It is a common practice for recent works in vision language cross-modal reasoning to adopt a binary or multi-choice classification formulation taking as input a set of source image(s) and textual query. In this work, we take a sober look at such an {``}unconditional{''} formulation in the sense that no prior knowledge ... | Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues | inproceedings | 932--946 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.67 | shen-etal-2022-parallel | Parallel Instance Query Network for Named Entity Recognition | Dublin, Ireland | 10.18653/v1/2022.acl-long.67 | https://aclanthology.org/2022.acl-long.67 | 2022 | Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructing type-specific queries manually to extract entities. This paradigm suffers from three issues. First, type-specific queries can only extract one ty... | Parallel Instance Query Network for Named Entity Recognition | inproceedings | 947--961 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.68 | liu-etal-2022-prophetchat | ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation | Dublin, Ireland | 10.18653/v1/2022.acl-long.68 | https://aclanthology.org/2022.acl-long.68 | 2022 | Typical generative dialogue models utilize the dialogue history to generate the response. However, since one dialogue utterance can often be appropriately answered by multiple distinct responses, generating a desired response solely based on the historical information is not easy. Intuitively, if the chatbot can forese... | {P}rophet{C}hat: Enhancing Dialogue Generation with Simulation of Future Conversation | inproceedings | 962--973 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.69 | yavuz-etal-2022-modeling | Modeling Multi-hop Question Answering as Single Sequence Prediction | Dublin, Ireland | 10.18653/v1/2022.acl-long.69 | https://aclanthology.org/2022.acl-long.69 | 2022 | Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work,... | Modeling Multi-hop Question Answering as Single Sequence Prediction | inproceedings | 974--990 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.70 | wu-etal-2022-learning | Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension | Dublin, Ireland | 10.18653/v1/2022.acl-long.70 | https://aclanthology.org/2022.acl-long.70 | 2022 | Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the tar... | Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension | inproceedings | 991--1000 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.71 | liu-etal-2022-multi-granularity | Multi-Granularity Structural Knowledge Distillation for Language Model Compression | Dublin, Ireland | 10.18653/v1/2022.acl-long.71 | https://aclanthology.org/2022.acl-long.71 | 2022 | Transferring the knowledge to a small model through distillation has raised great interest in recent years. Prevailing methods transfer the knowledge derived from mono-granularity language units (e.g., token-level or sample-level), which is not enough to represent the rich semantics of a text and may lose some vital kn... | Multi-Granularity Structural Knowledge Distillation for Language Model Compression | inproceedings | 1001--1011 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.72 | guo-etal-2022-auto | Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts | Dublin, Ireland | 10.18653/v1/2022.acl-long.72 | https://aclanthology.org/2022.acl-long.72 | 2022 | Human-like biases and undesired social stereotypes exist in large pretrained language models. Given the wide adoption of these models in real-world applications, mitigating such biases has become an emerging and important task. In this paper, we propose an automatic method to mitigate the biases in pretrained language ... | Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts | inproceedings | 1012--1023 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.73 | liu-etal-2022-go | Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals | Dublin, Ireland | 10.18653/v1/2022.acl-long.73 | https://aclanthology.org/2022.acl-long.73 | 2022 | Most dialog systems posit that users have figured out clear and specific goals before starting an interaction. For example, users have determined the departure, the destination, and the travel time for booking a flight. However, in many scenarios, limited by experience and knowledge, users may know what they need, but ... | Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals | inproceedings | 1024--1034 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.74 | li-etal-2022-semi | Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network | Dublin, Ireland | 10.18653/v1/2022.acl-long.74 | https://aclanthology.org/2022.acl-long.74 | 2022 | Supervised parsing models have achieved impressive results on in-domain texts. However, their performances drop drastically on out-of-domain texts due to the data distribution shift. The shared-private model has shown its promising advantages for alleviating this problem via feature separation, whereas prior works pay ... | Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network | inproceedings | 1035--1045 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.75 | zhou-srikumar-2022-closer | A Closer Look at How Fine-tuning Changes BERT | Dublin, Ireland | 10.18653/v1/2022.acl-long.75 | https://aclanthology.org/2022.acl-long.75 | 2022 | Given the prevalence of pre-trained contextualized representations in today{'}s NLP, there have been many efforts to understand what information they contain, and why they seem to be universally successful. The most common approach to use these representations involves fine-tuning them for an end task. Yet, how fine-tu... | A Closer Look at How Fine-tuning Changes {BERT} | inproceedings | 1046--1061 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.76 | hong-etal-2022-sentence | Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval | Dublin, Ireland | 10.18653/v1/2022.acl-long.76 | https://aclanthology.org/2022.acl-long.76 | 2022 | Training dense passage representations via contrastive learning has been shown effective for Open-Domain Passage Retrieval (ODPR). Existing studies focus on further optimizing by improving negative sampling strategy or extra pretraining. However, these studies keep unknown in capturing passage with internal representat... | Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval | inproceedings | 1062--1074 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.77 | sanyal-etal-2022-fairr | FaiRR: Faithful and Robust Deductive Reasoning over Natural Language | Dublin, Ireland | 10.18653/v1/2022.acl-long.77 | https://aclanthology.org/2022.acl-long.77 | 2022 | Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. Recent works show that such models can also produce the reasoning steps (i.e., the proof graph) that emulate the model{'}s logical reasoning process. Currently, these... | {F}ai{RR}: Faithful and Robust Deductive Reasoning over Natural Language | inproceedings | 1075--1093 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.78 | cheng-etal-2022-hitab | HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation | Dublin, Ireland | 10.18653/v1/2022.acl-long.78 | https://aclanthology.org/2022.acl-long.78 | 2022 | Tables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hierarchical tables. Hierarchical tables challenge numerical reasoning by complex hierarchical indexing, as well as implicit relationships of calculation and semantics. We present a new dataset, HiTa... | {H}i{T}ab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation | inproceedings | 1094--1110 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.79 | lu-etal-2022-doctor | Doctor Recommendation in Online Health Forums via Expertise Learning | Dublin, Ireland | 10.18653/v1/2022.acl-long.79 | https://aclanthology.org/2022.acl-long.79 | 2022 | Huge volumes of patient queries are daily generated on online health forums, rendering manual doctor allocation a labor-intensive task. To better help patients, this paper studies a novel task of doctor recommendation to enable automatic pairing of a patient to a doctor with relevant expertise. While most prior work in... | Doctor Recommendation in Online Health Forums via Expertise Learning | inproceedings | 1111--1123 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.80 | zhu-etal-2022-continual | Continual Prompt Tuning for Dialog State Tracking | Dublin, Ireland | 10.18653/v1/2022.acl-long.80 | https://aclanthology.org/2022.acl-long.80 | 2022 | A desirable dialog system should be able to continually learn new skills without forgetting old ones, and thereby adapt to new domains or tasks in its life cycle. However, continually training a model often leads to a well-known catastrophic forgetting issue. In this paper, we present Continual Prompt Tuning, a paramet... | Continual Prompt Tuning for Dialog State Tracking | inproceedings | 1124--1137 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.81 | fu-etal-2022-theres | There's a Time and Place for Reasoning Beyond the Image | Dublin, Ireland | 10.18653/v1/2022.acl-long.81 | https://aclanthology.org/2022.acl-long.81 | 2022 | Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture. For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understa... | There{'}s a Time and Place for Reasoning Beyond the Image | inproceedings | 1138--1149 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.82 | cheng-etal-2022-fortap | FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining | Dublin, Ireland | 10.18653/v1/2022.acl-long.82 | https://aclanthology.org/2022.acl-long.82 | 2022 | Tables store rich numerical data, but numerical reasoning over tables is still a challenge. In this paper, we find that the spreadsheet formula, a commonly used language to perform computations on numerical values in spreadsheets, is a valuable supervision for numerical reasoning in tables. Considering large amounts of... | {FORTAP}: Using Formulas for Numerical-Reasoning-Aware Table Pretraining | inproceedings | 1150--1166 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.83 | shankar-2022-multimodal | Multimodal fusion via cortical network inspired losses | Dublin, Ireland | 10.18653/v1/2022.acl-long.83 | https://aclanthology.org/2022.acl-long.83 | 2022 | Information integration from different modalities is an active area of research. Human beings and, in general, biological neural systems are quite adept at using a multitude of signals from different sensory perceptive fields to interact with the environment and each other. Recent work in deep fusion models via neural ... | Multimodal fusion via cortical network inspired losses | inproceedings | 1167--1178 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.84 | zhang-etal-2022-modeling | Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension | Dublin, Ireland | 10.18653/v1/2022.acl-long.84 | https://aclanthology.org/2022.acl-long.84 | 2022 | Procedural Multimodal Documents (PMDs) organize textual instructions and corresponding images step by step. Comprehending PMDs and inducing their representations for the downstream reasoning tasks is designated as Procedural MultiModal Machine Comprehension (M3C). In this study, we approach Procedural M3C at a fine-gra... | Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension | inproceedings | 1179--1189 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.85 | saha-etal-2022-explanation | Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning | Dublin, Ireland | 10.18653/v1/2022.acl-long.85 | https://aclanthology.org/2022.acl-long.85 | 2022 | Pre-trained sequence-to-sequence language models have led to widespread success in many natural language generation tasks. However, there has been relatively less work on analyzing their ability to generate structured outputs such as graphs. Unlike natural language, graphs have distinct structural and semantic properti... | Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning | inproceedings | 1190--1208 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.86 | basu-roy-chowdhury-etal-2022-unsupervised | Unsupervised Extractive Opinion Summarization Using Sparse Coding | Dublin, Ireland | 10.18653/v1/2022.acl-long.86 | https://aclanthology.org/2022.acl-long.86 | 2022 | Opinion summarization is the task of automatically generating summaries that encapsulate information expressed in multiple user reviews. We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner. SemAE uses dictionary learning to implicitly capture semantic informatio... | Unsupervised Extractive Opinion Summarization Using Sparse Coding | inproceedings | 1209--1225 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.87 | michalopoulos-etal-2022-lexsubcon | LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution | Dublin, Ireland | 10.18653/v1/2022.acl-long.87 | https://aclanthology.org/2022.acl-long.87 | 2022 | Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. Contextual word embedding models have achieved state-of-the-art results in the lexical substitution task by relying on contextual information extracted from the replaced word within the sentence. However, such m... | {L}ex{S}ub{C}on: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution | inproceedings | 1226--1236 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.88 | zhou-etal-2022-think | Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation | Dublin, Ireland | 10.18653/v1/2022.acl-long.88 | https://aclanthology.org/2022.acl-long.88 | 2022 | Implicit knowledge, such as common sense, is key to fluid human conversations. Current neural response generation (RG) models are trained to generate responses directly, omitting unstated implicit knowledge. In this paper, we present Think-Before-Speaking (TBS), a generative approach to first externalize implicit commo... | Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation | inproceedings | 1237--1252 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.89 | liu-etal-2022-flow | Flow-Adapter Architecture for Unsupervised Machine Translation | Dublin, Ireland | 10.18653/v1/2022.acl-long.89 | https://aclanthology.org/2022.acl-long.89 | 2022 | In this work, we propose a flow-adapter architecture for unsupervised NMT. It leverages normalizing flows to explicitly model the distributions of sentence-level latent representations, which are subsequently used in conjunction with the attention mechanism for the translation task. The primary novelties of our model a... | Flow-Adapter Architecture for Unsupervised Machine Translation | inproceedings | 1253--1266 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.90 | ghalandari-etal-2022-efficient | Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning | Dublin, Ireland | 10.18653/v1/2022.acl-long.90 | https://aclanthology.org/2022.acl-long.90 | 2022 | Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized models without the need for ground-truth training data, while allowing flexibility in the... | Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning | inproceedings | 1267--1280 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.91 | huang-etal-2022-tracing | Tracing Origins: Coreference-aware Machine Reading Comprehension | Dublin, Ireland | 10.18653/v1/2022.acl-long.91 | https://aclanthology.org/2022.acl-long.91 | 2022 | Machine reading comprehension is a heavily-studied research and test field for evaluating new pre-trained language models (PrLMs) and fine-tuning strategies, and recent studies have enriched the pre-trained language models with syntactic, semantic and other linguistic information to improve the performance of the model... | Tracing Origins: Coreference-aware Machine Reading Comprehension | inproceedings | 1281--1292 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.92 | khan-etal-2022-watclaimcheck | WatClaimCheck: A new Dataset for Claim Entailment and Inference | Dublin, Ireland | 10.18653/v1/2022.acl-long.92 | https://aclanthology.org/2022.acl-long.92 | 2022 | We contribute a new dataset for the task of automated fact checking and an evaluation of state of the art algorithms. The dataset includes claims (from speeches, interviews, social media and news articles), review articles published by professional fact checkers and premise articles used by those professional fact chec... | {W}at{C}laim{C}heck: A new Dataset for Claim Entailment and Inference | inproceedings | 1293--1304 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.93 | kamal-eddine-etal-2022-frugalscore | FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation | Dublin, Ireland | 10.18653/v1/2022.acl-long.93 | https://aclanthology.org/2022.acl-long.93 | 2022 | Fast and reliable evaluation metrics are key to R{\&}D progress. While traditional natural language generation metrics are fast, they are not very reliable. Conversely, new metrics based on large pretrained language models are much more reliable, but require significant computational resources. In this paper, we propos... | {F}rugal{S}core: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation | inproceedings | 1305--1318 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.94 | narayan-etal-2022-well | A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation | Dublin, Ireland | 10.18653/v1/2022.acl-long.94 | https://aclanthology.org/2022.acl-long.94 | 2022 | We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies. It builds on recently proposed plan-based neural generation models (FROST, Narayan et al, 2021) that are trained to first create a ... | A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation | inproceedings | 1319--1339 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.95 | yue-etal-2022-synthetic | Synthetic Question Value Estimation for Domain Adaptation of Question Answering | Dublin, Ireland | 10.18653/v1/2022.acl-long.95 | https://aclanthology.org/2022.acl-long.95 | 2022 | Synthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models. Since synthetic questions are often noisy in practice, existing work adapts scores from a pretrained QA (or QG) model as criteria to select high-quality questi... | Synthetic Question Value Estimation for Domain Adaptation of Question Answering | inproceedings | 1340--1351 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.96 | bai-etal-2022-better | Better Language Model with Hypernym Class Prediction | Dublin, Ireland | 10.18653/v1/2022.acl-long.96 | https://aclanthology.org/2022.acl-long.96 | 2022 | Class-based language models (LMs) have been long devised to address context sparsity in $n$-gram LMs. In this study, we revisit this approach in the context of neural LMs. We hypothesize that class-based prediction leads to an implicit context aggregation for similar words and thus can improve generalization for rare w... | Better Language Model with Hypernym Class Prediction | inproceedings | 1352--1362 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.97 | mehta-etal-2022-tackling | Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks | Dublin, Ireland | 10.18653/v1/2022.acl-long.97 | https://aclanthology.org/2022.acl-long.97 | 2022 | Easy access, variety of content, and fast widespread interactions are some of the reasons making social media increasingly popular. However, this rise has also enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. Detecting it is an important and ... | Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks | inproceedings | 1363--1380 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.98 | du-etal-2022-understanding | Understanding Gender Bias in Knowledge Base Embeddings | Dublin, Ireland | 10.18653/v1/2022.acl-long.98 | https://aclanthology.org/2022.acl-long.98 | 2022 | Knowledge base (KB) embeddings have been shown to contain gender biases. In this paper, we study two questions regarding these biases: how to quantify them, and how to trace their origins in KB? Specifically, first, we develop two novel bias measures respectively for a group of person entities and an individual person ... | Understanding Gender Bias in Knowledge Base Embeddings | inproceedings | 1381--1395 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.99 | arora-etal-2022-computational | Computational Historical Linguistics and Language Diversity in South Asia | Dublin, Ireland | 10.18653/v1/2022.acl-long.99 | https://aclanthology.org/2022.acl-long.99 | 2022 | South Asia is home to a plethora of languages, many of which severely lack access to new language technologies. This linguistic diversity also results in a research environment conducive to the study of comparative, contact, and historical linguistics{--}fields which necessitate the gathering of extensive data from man... | Computational Historical Linguistics and Language Diversity in {S}outh {A}sia | inproceedings | 1396--1409 |
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | Association for Computational Linguistics | May | 2022.acl-long.100 | ladhak-etal-2022-faithful | Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization | Dublin, Ireland | 10.18653/v1/2022.acl-long.100 | https://aclanthology.org/2022.acl-long.100 | 2022 | Despite recent progress in abstractive summarization, systems still suffer from faithfulness errors. While prior work has proposed models that improve faithfulness, it is unclear whether the improvement comes from an increased level of extractiveness of the model outputs as one naive way to improve faithfulness is to m... | Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization | inproceedings | 1410--1421 |
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