Homographs, words with different meanings but the same surface form, have
long caused difficulty for machine translation systems, as it is difficult to
select the correct translation based on the context. However, with the advent
of neural machine translation (NMT) systems, which can theoretically take into
account global sentential context, one may hypothesize that this problem has
been alleviated. In this paper, we first provide empirical evidence that
existing NMT systems in fact still have significant problems in properly
translating ambiguous words. We then proceed to describe methods, inspired by
the word sense disambiguation literature, that model the context of the input
word with context-aware word embeddings that help to differentiate the word
sense be- fore feeding it into the encoder. Experiments on three language pairs
demonstrate that such models improve the performance of NMT systems both in
terms of BLEU score and in the accuracy of translating homographs.

该研究利用词义消歧方法，在词输入编码器之前使用上下文感知的词嵌入来区分单词的多义性，解决神经机器翻译（NMT）系统在翻译歧义单词时存在的问题，实验表明该方法提高了 NMT 系统的翻译准确率。