Modeling Homophone Noise for Robust Neural Machine Translation
Wenjie Qin, Xianglin Li, Yuhui Sun, Deyi Xiong, Jianwei Cui, Bin Wang · 2021
In this paper, we propose a robust neural machine translation (NMT) framework to deal with homophone errors. The framework consists of a homophone noise detector and a syllable-aware NMT model. The detector identifies potential homophone errors in a textual sentence and converts them into syllables to form a mixed sequence that is then fed into the syllable-aware NMT. Extensive experiments on Chinese→English translation demonstrate that the proposed method not only significantly outperforms baselines on noisy test sets with homophone noise, but also achieves substantial improvements over them on clean texts.