Duplex Sequence-to-Sequence Learning for Reversible Machine Translation
Zaixiang Zheng, Hao Zhou, Shujian Huang, Jiajun Chen, Jingjing Xu, Lei Li
Abstract
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.
BibTeX
@inproceedings{
zheng2021duplex,
title={Duplex Sequence-to-Sequence Learning for Reversible Machine Translation},
author={Zaixiang Zheng and Hao Zhou and Shujian Huang and Jiajun Chen and Jingjing Xu and Lei Li},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=2BbDxFtDht7}
}