Papers with context-aware word embeddings

1 papers
Handling Homographs in Neural Machine Translation (N18-1)

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Challenge: Existing methods for MT have problems with translating homographs, as it is difficult to select the correct translation based on the context.
Approach: They propose to model the context of the input word with context-aware word embeddings that help to differentiate the word sense before feeding it into the encoder.
Outcome: The proposed models improve translation accuracy and BLEU score on three language pairs.

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