Modeling Homophone Noise for Robust Neural Machine Translation

Wenjie Qin; Xiang Li; Yuhui Sun; Deyi Xiong; Jianwei Cui; Bin Wang

ICASSP 2021accepted0 citations

Modeling Homophone Noise for Robust Neural Machine Translation

Wenjie Qin, Xiang Li, Yuhui Sun, Deyi Xiong, Jianwei Cui, Bin Wang

Abstract

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.

BibTeX

@inproceedings{icassp2021_modelinghomophon,
  title = {Modeling Homophone Noise for Robust Neural Machine Translation},
  author = {Wenjie Qin and Xiang Li and Yuhui Sun and Deyi Xiong and Jianwei Cui and Bin Wang},
  booktitle = {ICASSP 2021},
  year = {2021}
}