Neural machine translation (NMT) has a drawback in that can generate only
high-frequency words owing to the computational costs of the softmax function
in the output layer.
In Japanese-English NMT, Japanese predicate conjugation causes an increase in
vocabulary size. For example, one verb can have as many as 19 surface
varieties. In this research, we focus on predicate conjugation for compressing
the vocabulary size in Japanese. The vocabulary list is filled with the various
forms of verbs. We propose methods using predicate conjugation information
without discarding linguistic information. The proposed methods can generate
low-frequency words and deal with unknown words. Two methods were considered to
introduce conjugation information: the first considers it as a token
(conjugation token) and the second considers it as an embedded vector
(conjugation feature).
The results using these methods demonstrate that the vocabulary size can be
compressed by approximately 86.1% (Tanaka corpus) and the NMT models can output
the words not in the training data set. Furthermore, BLEU scores improved by
0.91 points in Japanese-to-English translation, and 0.32 points in
English-to-Japanese translation with ASPEC.

本研究提出了一种基于谓词词形变化的日本语 - 英语神经机器翻译方法，将词汇表中各种动词形式压缩为更少的单词，并通过属性词汇信息生成低频词汇和处理未知词汇，有效地压缩词汇表的大小，并在翻译中提高了 BLEU 分数。