Learning to Translate in Real-time with Neural Machine Translation

Jiatao Gu, Graham Neubig, Kyunghyun Cho, Victor O.K. Li


Abstract
Translating in real-time, a.k.a.simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods. We propose a neural machine translation (NMT) framework for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment. To trade off quality and delay, we extensively explore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting. Experiments against state-of-the-art baselines on two language pairs demonstrate the efficacy of the proposed framework both quantitatively and qualitatively.
Anthology ID:
E17-1099
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1053–1062
Language:
URL:
https://aclanthology.org/E17-1099
DOI:
Bibkey:
Cite (ACL):
Jiatao Gu, Graham Neubig, Kyunghyun Cho, and Victor O.K. Li. 2017. Learning to Translate in Real-time with Neural Machine Translation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1053–1062, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Learning to Translate in Real-time with Neural Machine Translation (Gu et al., EACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/E17-1099.pdf
Code
 nyu-dl/dl4mt-simul-trans