@inproceedings{zheng-etal-2019-simultaneous,
title = "Simultaneous Translation with Flexible Policy via Restricted Imitation Learning",
author = "Zheng, Baigong and
Zheng, Renjie and
Ma, Mingbo and
Huang, Liang",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1582/",
doi = "10.18653/v1/P19-1582",
pages = "5816--5822",
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 {\textquotedblleft}delay{\textquotedblright} token to the target vocabulary, and design a restricted dynamic oracle to greatly simplify training. Experiments on Chinese {\ensuremath{<}}-{\ensuremath{>}} 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."
}
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<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 \ensuremath<-\ensuremath> 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.</abstract>
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%0 Conference Proceedings
%T Simultaneous Translation with Flexible Policy via Restricted Imitation Learning
%A Zheng, Baigong
%A Zheng, Renjie
%A Ma, Mingbo
%A Huang, Liang
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zheng-etal-2019-simultaneous
%X 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 \ensuremath<-\ensuremath> 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.
%R 10.18653/v1/P19-1582
%U https://aclanthology.org/P19-1582/
%U https://doi.org/10.18653/v1/P19-1582
%P 5816-5822
Markdown (Informal)
[Simultaneous Translation with Flexible Policy via Restricted Imitation Learning](https://aclanthology.org/P19-1582/) (Zheng et al., ACL 2019)
ACL