@inproceedings{aoki-etal-2020-text,
    title = "Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation",
    author = "Aoki, Takumi  and
      Kitada, Shunsuke  and
      Iyatomi, Hitoshi",
    editor = "Shmueli, Boaz  and
      Huang, Yin Jou",
    booktitle = "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",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.aacl-srw.1/",
    doi = "10.18653/v1/2020.aacl-srw.1",
    pages = "1--7",
    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 {\ensuremath{\beta}}-variational auto-encoder ({\ensuremath{\beta}} -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."
}