CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
One Sentence One Model for Neural Machine Translation
Li, Xiaoqing; Zhang, Jiajun; Zong, Chengqing
2018
Conference NameLREC
Conference Date2018-5
Conference PlaceJapan
Abstract

Neural machine translation (NMT) becomes a new state of the art and achieves promising translation performance using a simple encoder-decoder neural network. This neural network is trained once on the parallel corpus and the fixed network is used to translate all the test sentences. We argue that the general fixed network parameters cannot best fit each specific testing sentences. In this paper, we propose the dynamic NMT which learns a general network as usual, and then fine-tunes the network for each test sentence. The fine-tune work is done on a small set of the bilingual training data that is obtained through similarity search according to the test sentence. Extensive experiments demonstrate that this method can significantly improve the translation performance, especially when highly similar sentences are available.

Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23204
Collection模式识别国家重点实验室_自然语言处理
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Xiaoqing,Zhang, Jiajun,Zong, Chengqing. One Sentence One Model for Neural Machine Translation[C],2018.
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