A Compact and Language-Sensitive Multilingual Translation Method | |
Yining Wang; Long Zhou; Jiajun Zhang; Feifei Zhai; Jingfang Xu; Chengqing Zong | |
2019 | |
会议日期 | 2019 |
会议地点 | Florence, Italia |
英文摘要 | 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. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/26133] |
专题 | 自动化研究所_模式识别国家重点实验室_自然语言处理团队 |
通讯作者 | Jiajun Zhang |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yining Wang,Long Zhou,Jiajun Zhang,et al. A Compact and Language-Sensitive Multilingual Translation Method[C]. 见:. Florence, Italia. 2019. |
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