Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Error Detection in Large-Scale Natural Language Understanding Systems
  Using Transformer Models   Large-scale conversational assistants like Alexa, Siri, Cortana and Google
Assistant process every utterance using multiple models for domain, intent and
named entity recognition. Given the decoupled nature of model development and
large traffic volumes, it is extremely difficult to identify utterances
processed erroneously by such systems. We address this challenge to detect
domain classification errors using offline Transformer models. We combine
utterance encodings from a RoBERTa model with the Nbest hypothesis produced by
the production system. We then fine-tune end-to-end in a multitask setting
using a small dataset of humanannotated utterances with domain classification
errors. We tested our approach for detecting misclassifications from one domain
that accounts for <0.5% of the traffic in a large-scale conversational AI
system. Our approach achieves an F1 score of 30% outperforming a bi- LSTM
baseline by 16.9% and a standalone RoBERTa model by 4.8%. We improve this
further by 2.2% to 32.2% by ensembling multiple models.

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The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: math
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The categories of this paper are: ao-sci