In Neural Machine Translation (NMT), the decoder can capture the features of the entire prediction history with neural connections and representations. This means that partial hypotheses with different prefixes will be regarded differently no matter how similar they are. However, this might be inefficient since some partial hypotheses can contain only local differences that will not influence future predictions. In this work, we introduce recombination in NMT decoding based on the concept of the "equivalence" of partial hypotheses. Heuristically, we use a simple $n$-gram suffix based equivalence function and adapt it into beam search decoding. Through experiments on large-scale Chinese-to-English and English-to-Germen translation tasks, we show that the proposed method can obtain similar translation quality with a smaller beam size, making NMT decoding more efficient.

本文介绍了在神经机器翻译解码中利用部分假设等价性的概念，引入了重新组合，使用简单的n-gram后缀等价函数并将其调整为波束搜索解码，经过中英文和英德文大规模翻译任务的实验表明，该方法可以在更小的波束大小下获得相似的翻译质量，从而使NMT解码更加高效。

探索重组以实现神经机器翻译有效解码