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Addressing Troublesome Words in Neural Machine Translation
Zhao, Yang1; Zhang, Jiajun1; He, Zhongjun2; Zong, Chengqing1; Wu, Hua2
Conference NameEMNLP
Conference Date2018-11
Conference PlaceBrussels, Belgium

One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words. To address this problem, we propose a novel memoryenhanced NMT method. First, we investigate different strategies to define and detect the troublesome words. Then, a contextual memory is constructed to memorize which target words should be produced in what situations. Finally, we design a hybrid model to dynamically access the contextual memory so as to correctly translate the troublesome words. The extensive experiments on Chineseto-English and English-to-German translation tasks demonstrate that our method significantly outperforms the strong baseline models in translation quality, especially in handling troublesome words.

Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhao, Yang,Zhang, Jiajun,He, Zhongjun,et al. Addressing Troublesome Words in Neural Machine Translation[C],2018.
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