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- W3034674003 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, Glyphs, in ideographic languages could carry rich semantics. We present a multi-modal model, 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." @default.
- W3034674003 created "2020-06-19" @default.
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- W3034674003 date "2020-01-01" @default.
- W3034674003 modified "2023-10-16" @default.
- W3034674003 title "Glyph2Vec: Learning Chinese Out-of-Vocabulary Word Embedding from Glyphs" @default.
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- W3034674003 doi "https://doi.org/10.18653/v1/2020.acl-main.256" @default.
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