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.
@article{arxiv.1906.01268,
title = {Exploiting Sentential Context for Neural Machine Translation},
author = {Xing Wang and Zhaopeng Tu and Longyue Wang and Shuming Shi},
journal= {arXiv preprint arXiv:1906.01268},
year = {2019}
}