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arXiv · 1806.03661

Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation

Fahim Dalvi·Nadir Durrani·Hassan Sajjad·Stephan Vogel
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Abstract

We address the problem of simultaneous translation by modifying the Neural MT decoder to operate with dynamically built encoder and attention. We propose a tunable agent which decides the best segmentation strategy for a user-defined BLEU loss and Average Proportion (AP) constraint. Our agent outperforms previously proposed Wait-if-diff and Wait-if-worse agents (Cho and Esipova, 2016) on BLEU with a lower latency. Secondly we proposed data-driven changes to Neural MT training to better match the incremental decoding framework.

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Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Stephan Vogel. 2018-06-10. Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation. https://arxiv.org/abs/1806.03661

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