@inproceedings{chen-etal-2020-glyph2vec,
    title = "{G}lyph2{V}ec: Learning {C}hinese Out-of-Vocabulary Word Embedding from Glyphs",
    author = "Chen, Hong-You  and
      Yu, Sz-Han  and
      Lin, Shou-de",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.acl-main.256/",
    doi = "10.18653/v1/2020.acl-main.256",
    pages = "2865--2871",
    abstract = "Chinese NLP applications that rely on large text often contain huge amounts of vocabulary which are sparse in corpus. We show that characters' written form, \textit{Glyphs}, in ideographic languages could carry rich semantics. We present a multi-modal model, \textit{Glyph2Vec}, to tackle Chinese out-of-vocabulary word embedding problem. Glyph2Vec extracts visual features from word glyphs to expand current word embedding space for out-of-vocabulary word embedding, without the need of accessing any corpus, which is useful for improving Chinese NLP systems, especially for low-resource scenarios. Experiments across different applications show the significant effectiveness of our model."
}