CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Three Strategies to Improve One-to-Many Multilingual Translation
Wang, Yining1; Zhang, Jiajun1; Zhai, Feifei2; Xu, Jingfang2; Zong, Chengqing1
2018
Conference NameEMNLP
Conference Date2018-11
Conference PlaceBrussels, Belgium
Abstract

Due to the benefits of model compactness, multilingual translation (including many-toone, many-to-many and one-to-many) based on a universal encoder-decoder architecture attracts more and more attention. However, previous studies show that one-to-many translation based on this framework cannot perform on par with the individually trained models. In this work, we introduce three strategies to improve one-to-many multilingual translation by balancing the shared and unique features. Within the architecture of one decoder for all target languages, we first exploit the use of unique initial states for different target languages. Then, we employ language-dependent positional embeddings. Finally and especially, we propose to divide the hidden cells of the decoder into shared and language-dependent ones. The extensive experiments demonstrate that our proposed methods can obtain remarkable improvements over the strong baselines. Moreover, our strategies can achieve comparable or even better performance than the individually trained translation models.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23197
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.搜狗
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Yining,Zhang, Jiajun,Zhai, Feifei,et al. Three Strategies to Improve One-to-Many Multilingual Translation[C],2018.
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