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
\emph{dual-NMT}. Experiments show that dual-NMT works very well on
English$\leftrightarrow$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.

提出了一种双向学习机制（dual-learning mechanism），通过强化学习过程，使用一个代理模型代表原始任务模型，另一个代理模型代表对偶任务模型，以无监督学习方式自动从无标注数据中学习。实验证明，该方法在英法翻译中表现良好，尤其是在使用单语数据的情况下。