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arXiv · 1610.00388

Learning to Translate in Real-time with Neural Machine Translation

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

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Jiatao Gu, Graham Neubig, Kyunghyun Cho, Victor O. K. Li. 2017-01-10. Learning to Translate in Real-time with Neural Machine Translation. https://arxiv.org/abs/1610.00388

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