Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Augmentation Approaches in Natural Language Processing: A Survey   As an effective strategy, data augmentation (DA) alleviates data scarcity
scenarios where deep learning techniques may fail. It is widely applied in
computer vision then introduced to natural language processing and achieves
improvements in many tasks. One of the main focuses of the DA methods is to
improve the diversity of training data, thereby helping the model to better
generalize to unseen testing data. In this survey, we frame DA methods into
three categories based on the diversity of augmented data, including
paraphrasing, noising, and sampling. Our paper sets out to analyze DA methods
in detail according to the above categories. Further, we also introduce their
applications in NLP tasks as well as the challenges.

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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: eess