Neural Transduction for Multilingual Lexical Translation
Dylan Lewis, Winston M.C. Wu, Arya D. McCarthy, David Yarowsky · 2020
We present a method for completing multilingual translation dictionaries.Our probabilistic approach can synthesize new word forms, allowing it to operate in settings where correct translations have not been observed in text (cf.cross-lingual embeddings).In addition, we propose an approximate Maximum Mutual Information (MMI) decoding objective to further improve performance in both many-to-one and one-to-one word level translation tasks where we use either multiple input languages for a single target language or more typical single language pair translation.The model is trained in a many-to-many setting, where it can leverage information from related languages to predict words in each of its many target languages.We focus on 6 languages: French, Spanish, Italian, Portuguese, Romanian, and Turkish.When indirect multilingual information is available, ensembling with mixture-of-experts as well as incorporating related languages leads to a 27% relative improvement in whole-word accuracy of predictions over a single-source baseline.To seed the completion when multilingual data is unavailable, it is better to decode with an MMI objective.