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        <title>Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation</title>
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        <namePart type="family">Kitada</namePart>
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    <name type="personal">
        <namePart type="given">Hitoshi</namePart>
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            <namePart type="given">Boaz</namePart>
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    <abstract>We propose a new character-based text classification framework for non-alphabetic languages, such as Chinese and Japanese. Our framework consists of a variational character encoder (VCE) and character-level text classifier. The VCE is composed of a β-variational auto-encoder (β -VAE) that learns the proposed glyph-aware disentangled character embedding (GDCE). Since our GDCE provides zero-mean unit-variance character embeddings that are dimensionally independent, it is applicable for our interpretable data augmentation, namely, semantic sub-character augmentation (SSA). In this paper, we evaluated our framework using Japanese text classification tasks at the document- and sentence-level. We confirmed that our GDCE and SSA not only provided embedding interpretability but also improved the classification performance. Our proposal achieved a competitive result to the state-of-the-art model while also providing model interpretability.</abstract>
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%0 Conference Proceedings
%T Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation
%A Aoki, Takumi
%A Kitada, Shunsuke
%A Iyatomi, Hitoshi
%Y Shmueli, Boaz
%Y Huang, Yin Jou
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F aoki-etal-2020-text
%X We propose a new character-based text classification framework for non-alphabetic languages, such as Chinese and Japanese. Our framework consists of a variational character encoder (VCE) and character-level text classifier. The VCE is composed of a β-variational auto-encoder (β -VAE) that learns the proposed glyph-aware disentangled character embedding (GDCE). Since our GDCE provides zero-mean unit-variance character embeddings that are dimensionally independent, it is applicable for our interpretable data augmentation, namely, semantic sub-character augmentation (SSA). In this paper, we evaluated our framework using Japanese text classification tasks at the document- and sentence-level. We confirmed that our GDCE and SSA not only provided embedding interpretability but also improved the classification performance. Our proposal achieved a competitive result to the state-of-the-art model while also providing model interpretability.
%U https://aclanthology.org/2020.aacl-srw.1
%P 1-7

Markdown (Informal)

[Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation](https://aclanthology.org/2020.aacl-srw.1) (Aoki et al., AACL 2020)

• Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation (Aoki et al., AACL 2020)

ACL

• Takumi Aoki, Shunsuke Kitada, and Hitoshi Iyatomi. 2020. Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, pages 1–7, Suzhou, China. Association for Computational Linguistics.