Abstract
Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, e.g., dual learning, and bidirectional decoding with one agent decod- ing from left to right and the other decoding in the opposite direction. In this paper, we extend the training framework to the multi-agent sce- nario by introducing diverse agents in an in- teractive updating process. At training time, each agent learns advanced knowledge from others, and they work together to improve translation quality. Experimental results on NIST Chinese-English, IWSLT 2014 German- English, WMT 2014 English-German and large-scale Chinese-English translation tasks indicate that our approach achieves absolute improvements over the strong baseline sys- tems and shows competitive performance on all tasks.- Anthology ID:
- D19-1079
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 856–865
- Language:
- URL:
- https://aclanthology.org/D19-1079
- DOI:
- 10.18653/v1/D19-1079
- Cite (ACL):
- Tianchi Bi, Hao Xiong, Zhongjun He, Hua Wu, and Haifeng Wang. 2019. Multi-agent Learning for Neural Machine Translation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 856–865, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Multi-agent Learning for Neural Machine Translation (Bi et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-1079.pdf