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- W4290442210 abstract "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 word-character 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 +2.1-11.4 BLEU 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." @default.
- W4290442210 created "2022-08-07" @default.
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- W4290442210 date "2016-04-04" @default.
- W4290442210 modified "2023-09-25" @default.
- W4290442210 title "Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models" @default.
- W4290442210 doi "https://doi.org/10.48550/arxiv.1604.00788" @default.
- W4290442210 hasPublicationYear "2016" @default.
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