报告内容摘要：

While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a dual-learning mechanism, which can enable an NMT system to automatically learn from unlabeled data through a dual-learning game. This mechanism is inspired by the following observation: any machine translation task has a dual task, e.g., English-to-French translation (primal) versus French-to-English translation (dual); the primal and dual tasks can form a closed loop, and generate informative feedback signals to train the translation models, even if without the involvement of a human labeler. In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process (e.g., the language-model likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e.g., using the policy gradient methods). We call the corresponding approach to neural machine translation dual-NMT. Experiments show that dual-NMT works very well on English?French translation; especially, by learning from monolingual data (with 10% bilingual data for warm start), it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task.

报告人简介：

贺笛分别于2009年，2013年获得澳门太阳娱乐网站官网学士学位和2138cn太阳集团古天乐信息科学与技术学院硕士学位，此后就职于微软亚洲研究院机器学习组，现为2138cn太阳集团古天乐信息科学与技术学院在读博士。贺笛的研究兴趣主要集中于深度学习，增强学习与博弈论方向，在国际学术会议NIPS，IJCAI，EC发表多篇文章，并多次担任相关会议审稿人工作，曾获2138cn太阳集团古天乐本科生学术希望之星等荣誉。
 
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