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arXiv · 1605.04569

Syntactically Guided Neural Machine Translation

Felix Stahlberg·Eva Hasler·Aurelien Waite·Bill Byrne
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Abstract

We investigate the use of hierarchical phrase-based SMT lattices in end-to-end neural machine translation (NMT). Weight pushing transforms the Hiero scores for complete translation hypotheses, with the full translation grammar score and full n-gram language model score, into posteriors compatible with NMT predictive probabilities. With a slightly modified NMT beam-search decoder we find gains over both Hiero and NMT decoding alone, with practical advantages in extending NMT to very large input and output vocabularies.

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Felix Stahlberg, Eva Hasler, Aurelien Waite, Bill Byrne. 2016-05-15. Syntactically Guided Neural Machine Translation. https://doi.org/10.18653/v1%2Fp16-2049

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