Papers with character n-gram based embedding

1 papers
Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation (2020.findings-emnlp)

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Challenge: Multilingual data is more beneficial for NMT models that translate from the LRL to a target language than those that translate into the LLLs.
Approach: They propose a decoder that embeds character n-grams into NMT models that translate from an LRL to a target language.
Outcome: The proposed decoder improves the performance of NMT models that translate from an LRL to a target language.

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