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        <title>Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations</title>
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        <namePart type="given">Sosuke</namePart>
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            <namePart type="given">Marilyn</namePart>
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        <name type="personal">
            <namePart type="given">Heng</namePart>
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    <abstract>We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are replaced with other words with paradigmatic relations. We stochastically replace words with other words that are predicted by a bi-directional language model at the word positions. Words predicted according to a context are numerous but appropriate for the augmentation of the original words. Furthermore, we retrofit a language model with a label-conditional architecture, which allows the model to augment sentences without breaking the label-compatibility. Through the experiments for six various different text classification tasks, we demonstrate that the proposed method improves classifiers based on the convolutional or recurrent neural networks.</abstract>
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%0 Conference Proceedings
%T Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
%A Kobayashi, Sosuke
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kobayashi-2018-contextual
%X We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are replaced with other words with paradigmatic relations. We stochastically replace words with other words that are predicted by a bi-directional language model at the word positions. Words predicted according to a context are numerous but appropriate for the augmentation of the original words. Furthermore, we retrofit a language model with a label-conditional architecture, which allows the model to augment sentences without breaking the label-compatibility. Through the experiments for six various different text classification tasks, we demonstrate that the proposed method improves classifiers based on the convolutional or recurrent neural networks.
%R 10.18653/v1/N18-2072
%U https://aclanthology.org/N18-2072
%U https://doi.org/10.18653/v1/N18-2072
%P 452-457

Markdown (Informal)

[Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://aclanthology.org/N18-2072) (Kobayashi, NAACL 2018)

• Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations (Kobayashi, NAACL 2018)

ACL

• Sosuke Kobayashi. 2018. Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 452–457, New Orleans, Louisiana. Association for Computational Linguistics.