Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
Minh-Thang Luong, Christopher D. Manning · 2016
Nearly all previous work on neural machine translation (NMT) has used quite restricted vocabularies, perhaps with a subsequent method to patch in unknown words.This paper presents a novel wordcharacter solution to achieving open vocabulary NMT.We build hybrid systems that translate mostly at the word level and consult the character components for rare words.Our character-level recurrent neural networks compute source word representations and recover unknown target words when needed.The twofold advantage of such a hybrid approach is that it is much faster and easier to train than character-based ones; at the same time, it never produces unknown words as in the case of word-based models.On the WMT'15 English to Czech translation task, this hybrid approach offers an addition boost of +2.1-11.4BLEU points over models that already handle unknown words.Our best system achieves a new state-of-the-art result with 20.7 BLEU score.We demonstrate that our character models can successfully learn to not only generate well-formed words for Czech, a highly-inflected language with a very complex vocabulary, but also build correct representations for English source words.