2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-8.1
Paper Title MODELING HOMOPHONE NOISE FOR ROBUST NEURAL MACHINE TRANSLATION
Authors Wenjie Qin, Soochow University, China; Xiang Li, Yuhui Sun, Xiaomi AI Lab, China; Deyi Xiong, Tianjin University, China; Jianwei Cui, Bin Wang, Xiaomi AI Lab, China
SessionHLT-8: Speech Translation 2: Aspects
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Human Language Technology: [HLT-MTSW] Machine Translation for Spoken and Written Language
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.