CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Addressing Troublesome Words in Neural Machine Translation
Zhao, Yang1; Zhang, Jiajun1; He, Zhongjun2; Zong, Chengqing1; Wu, Hua2
2018
Conference NameEMNLP
Conference Date2018-11
Conference PlaceBrussels, Belgium
Abstract

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.

Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23196
Collection模式识别国家重点实验室_自然语言处理
Affiliation1.中国科学院自动化研究所
2.百度
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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