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arXiv · 1704.03169

Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation

Raphael Shu·Hideki Nakayama
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Abstract

For extended periods of time, sequence generation models rely on beam search algorithm to generate output sequence. However, the correctness of beam search degrades when the a model is over-confident about a suboptimal prediction. In this paper, we propose to perform minimum Bayes-risk (MBR) decoding for some extra steps at a later stage. In order to speed up MBR decoding, we compute the Bayes risks on GPU in batch mode. In our experiments, we found that MBR reranking works with a large beam size. Later-stage MBR decoding is shown to outperform simple MBR reranking in machine translation tasks.

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Raphael Shu, Hideki Nakayama. 2017-04-11. Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation. https://arxiv.org/abs/1704.03169

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