Simultaneous Translation with Flexible Policy via Restricted Imitation Learning

Baigong Zheng, Renjie Zheng, Mingbo Ma, Liang Huang


Abstract
Simultaneous translation is widely useful but remains one of the most difficult tasks in NLP. Previous work either uses fixed-latency policies, or train a complicated two-staged model using reinforcement learning. We propose a much simpler single model that adds a “delay” token to the target vocabulary, and design a restricted dynamic oracle to greatly simplify training. Experiments on Chinese <-> English simultaneous translation show that our work leads to flexible policies that achieve better BLEU scores and lower latencies compared to both fixed and RL-learned policies.
Anthology ID:
P19-1582
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5816–5822
Language:
URL:
https://aclanthology.org/P19-1582
DOI:
10.18653/v1/P19-1582
Bibkey:
Cite (ACL):
Baigong Zheng, Renjie Zheng, Mingbo Ma, and Liang Huang. 2019. Simultaneous Translation with Flexible Policy via Restricted Imitation Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5816–5822, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning (Zheng et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P19-1582.pdf
Video:
 https://vimeo.com/385434770