Neural machine translation (NMT), a new approach to machine translation, has
achieved promising results comparable to those of traditional approaches such
as statistical machine translation (SMT). Despite its recent success, NMT
cannot handle a larger vocabulary because the training complexity and decoding
complexity proportionally increase with the number of target words. This
problem becomes even more serious when translating patent documents, which
contain many technical terms that are observed infrequently. In this paper, we
propose to select phrases that contain out-of-vocabulary words using the
statistical approach of branching entropy. This allows the proposed NMT system
to be applied to a translation task of any language pair without any
language-specific knowledge about technical term identification. The selected
phrases are then replaced with tokens during training and post-translated by
the phrase translation table of SMT. Evaluation on Japanese-to-Chinese,
Chinese-to-Japanese, Japanese-to-English and English-to-Japanese patent
sentence translation proved the effectiveness of phrases selected with
branching entropy, where the proposed NMT model achieves a substantial
improvement over a baseline NMT model without our proposed technique. Moreover,
the number of translation errors of under-translation by the baseline NMT model
without our proposed technique reduces to around half by the proposed NMT
model.

本文基于分支熵的统计方法，提出一种扩展词汇表的神经机器翻译方法，使其适用于翻译专利文件等包含大量罕见技术术语的情况。通过对选定的短语进行 SMT 短语翻译表替换，达到显著的机器翻译错误减少和翻译精度提升。