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arXiv · 1906.01268

Exploiting Sentential Context for Neural Machine Translation

Xing Wang·Zhaopeng Tu·Longyue Wang·Shuming Shi
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Abstract

In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT). Specifically, we first show that a shallow sentential context extracted from the top encoder layer only, can improve translation performance via contextualizing the encoding representations of individual words. Next, we introduce a deep sentential context, which aggregates the sentential context representations from all the internal layers of the encoder to form a more comprehensive context representation. Experimental results on the WMT14 English-to-German and English-to-French benchmarks show that our model consistently improves performance over the strong TRANSFORMER model (Vaswani et al., 2017), demonstrating the necessity and effectiveness of exploiting sentential context for NMT.

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Xing Wang, Zhaopeng Tu, Longyue Wang, Shuming Shi. 2019-06-04. Exploiting Sentential Context for Neural Machine Translation. https://arxiv.org/abs/1906.01268

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