CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Addressing the Under-translation Problem from the Entropy Perspective
Zhao, Yang1; Zhang, Jiajun1; Zong, Chengqing1; He, Zhongjun2; Wu, Hua2
2019
Conference NameAAAI
Conference Date2019
Conference PlaceHonolulu, Hawaii, USA
Abstract

Neural Machine Translation (NMT) has drawn much attention
due to its promising translation performance in recent
years. However, the under-translation problem still remains a
big challenge. In this paper, we focus on the under-translation
problem and attempt to find out what kinds of words are
more likely to be ignored. Through analysis, we observe that
a source word with a larger translation entropy is more inclined
to be dropped. To address this problem, we proposed
a coarse-to-fine framework, in which we first introduce a
simple strategy to reduce the entropy of high-entropy words
through constructing the pseudo target sentences. Then we
propose three methods, including pre-training method, multitask
method and two-pass method, to encourage the neural
model to focus on these high-entropy words. Experimental
results on various translation tasks show that our method can
significantly improve the translation quality and substantially
reduce the under-translation cases of high-entropy words.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23194
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,Zong, Chengqing,et al. Addressing the Under-translation Problem from the Entropy Perspective[C],2019.
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