Duplex Sequence-to-Sequence Learning for Reversible Machine Translation

Sequence-to-sequence learning naturally has two directions. How to effectively utilize supervision signals from both directions? Existing approaches either require two separate models, or a multitask-learned model but with inferior performance. In this paper, we propose REDER (Reversible Duplex Transformer), a parameter-efficient model and apply it to machine translation. Either end of REDER can simultaneously input and output a distinct language. Thus REDER enables {\em reversible machine translation} by simply flipping the input and output ends. Experiments verify that REDER achieves the first success of reversible machine translation, which helps outperform its multitask-trained baselines by up to 1. 3 BLEU.

Authors

• Zaixiang Zheng
• Hao Zhou Naval University of Engineering
• Shujian Huang Nanjing University
• Jiajun Chen School of Robotics, Hunan University
• Jingjing Xu
• Lei Li Cleveland State University

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