Coverage Embedding Models for Neural Machine Translation

Haitao Mi; Baskaran Sankaran; Zhiguo Wang; Abe Ittycheriah

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arXiv · 1605.03148

Coverage Embedding Models for Neural Machine Translation

Haitao Mi·Baskaran Sankaran·Zhiguo Wang·Abe Ittycheriah

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Abstract

In this paper, we enhance the attention-based neural machine translation (NMT) by adding explicit coverage embedding models to alleviate issues of repeating and dropping translations in NMT. For each source word, our model starts with a full coverage embedding vector to track the coverage status, and then keeps updating it with neural networks as the translation goes. Experiments on the large-scale Chinese-to-English task show that our enhanced model improves the translation quality significantly on various test sets over the strong large vocabulary NMT system.

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BibTeX RIS

Haitao Mi, Baskaran Sankaran, Zhiguo Wang, Abe Ittycheriah. 2016-08-29. Coverage Embedding Models for Neural Machine Translation. https://arxiv.org/abs/1605.03148

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