Yuuki Sekizawa


2017

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Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language
Yuuki Sekizawa | Tomoyuki Kajiwara | Mamoru Komachi
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

Neural machine translation (NMT) produces sentences that are more fluent than those produced by statistical machine translation (SMT). However, NMT has a very high computational cost because of the high dimensionality of the output layer. Generally, NMT restricts the size of vocabulary, which results in infrequent words being treated as out-of-vocabulary (OOV) and degrades the performance of the translation. In evaluation, we achieved a statistically significant BLEU score improvement of 0.55-0.77 over the baselines including the state-of-the-art method.