Neural machine translation (NMT) is notoriously sensitive to noises, but
noises are almost inevitable in practice. One special kind of noise is the
homophone noise, where words are replaced by other words with similar
pronunciations. We propose to improve the robustness of NMT to homophone noises
by 1) jointly embedding both textual and phonetic information of source
sentences, and 2) augmenting the training dataset with homophone noises.
Interestingly, to achieve better translation quality and more robustness, we
found that most (though not all) weights should be put on the phonetic rather
than textual information. Experiments show that our method not only
significantly improves the robustness of NMT to homophone noises, but also
surprisingly improves the translation quality on some clean test sets.

本篇论文研究了神经机器翻译中的一种特殊噪声 - 同音替换，提出将源语言句子的文本和音素信息进行联合嵌入，并使用同音替换扩充训练数据集来提高模型对同音替换的鲁棒性，实验结果表明，放置在音素信息上的大多数权重可能比文本信息更有利于提高翻译质量和模型鲁棒性。