CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
A Compact and Language-Sensitive Multilingual Translation Method
Yining Wang; Long Zhou; Jiajun Zhang; Feifei Zhai; Jingfang Xu; Chengqing Zong
Conference NameACL-2019
Conference Date2019
Conference PlaceFlorence, Italia

Multilingual neural machine translation (Multi-NMT) with one encoder-decoder model has made remarkable progress due to its simple deployment. However, this multilingual translation paradigm does not make full use of language commonality and parameter sharing between encoder and decoder. Furthermore, this kind of paradigm cannot outperform the individual models trained on bilingual corpus in most cases. In this paper, we propose a compact and language-sensitive method for multilingual translation. To maximize parameter sharing, we first present a universal representor to replace both encoder and decoder models. To make the representor sensitive for specific languages, we further introduce language-sensitive embedding, attention, and discriminator with the ability to enhance model performance. We verify our methods on various translation scenarios, including one-to-many, many-to-many and zero-shot. Extensive experiments demonstrate that our proposed methods remarkably outperform strong standard multilingual translation systems on WMT and IWSLT datasets. Moreover, we find that our model is especially helpful in low-resource and zero-shot translation scenarios.

Document Type会议论文
Corresponding AuthorJiajun Zhang
Recommended Citation
GB/T 7714
Yining Wang,Long Zhou,Jiajun Zhang,et al. A Compact and Language-Sensitive Multilingual Translation Method[C],2019.
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