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arXiv · 1906.01135

Simultaneous Translation with Flexible Policy via Restricted Imitation Learning

Baigong Zheng·Renjie Zheng·Mingbo Ma·Liang Huang
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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<->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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Baigong Zheng, Renjie Zheng, Mingbo Ma, Liang Huang. 2019-06-04. Simultaneous Translation with Flexible Policy via Restricted Imitation Learning. https://arxiv.org/abs/1906.01135

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