Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DAGA: Data Augmentation with a Generation Approach for Low-resource
  Tagging Tasks   Data augmentation techniques have been widely used to improve machine
learning performance as they enhance the generalization capability of models.
In this work, to generate high quality synthetic data for low-resource tagging
tasks, we propose a novel augmentation method with language models trained on
the linearized labeled sentences. Our method is applicable to both supervised
and semi-supervised settings. For the supervised settings, we conduct extensive
experiments on named entity recognition (NER), part of speech (POS) tagging and
end-to-end target based sentiment analysis (E2E-TBSA) tasks. For the
semi-supervised settings, we evaluate our method on the NER task under the
conditions of given unlabeled data only and unlabeled data plus a knowledge
base. The results show that our method can consistently outperform the
baselines, particularly when the given gold training data are less.

23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
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