In order to capture rich language phenomena, neural machine translation
models have to use a large vocabulary size, which requires high computing time
and large memory usage. In this paper, we alleviate this issue by introducing a
sentence-level or batch-level vocabulary, which is only a very small sub-set of
the full output vocabulary. For each sentence or batch, we only predict the
target words in its sentence-level or batch-level vocabulary. Thus, we reduce
both the computing time and the memory usage. Our method simply takes into
account the translation options of each word or phrase in the source sentence,
and picks a very small target vocabulary for each sentence based on a
word-to-word translation model or a bilingual phrase library learned from a
traditional machine translation model. Experimental results on the large-scale
English-to-French task show that our method achieves better translation
performance by 1 BLEU point over the large vocabulary neural machine
translation system of Jean et al. (2015).

本文提出了一种句子级或批量级词汇表来缓解神经机器翻译模型中需使用大词汇表的问题。此方法根据源句子中每个单词或短语的翻译选项，选择非常小的目标词汇表，并基于单词翻译模型或双语短语库来实现。实验结果表明，该方法在英法翻译任务中的 BLEU 分数比 Jean 等人使用大词汇表的神经机器翻译系统高 1 个点。