NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021
Hideya Mino, Kazutaka Kinugawa, Hitoshi Ito, Isao Goto, Ichiro Yamada, Takenobu Tokunaga · 2021
This paper describes the system of our team (NHK) for the WAT 2021 Japanese↔English restricted machine translation task.In this task, the aim is to improve quality while maintaining consistent terminology for scientific paper translation.This task has a unique feature, where some words in a target sentence are given in addition to a source sentence.In this paper, we use a lexically-constrained neural machine translation (NMT), which concatenates the source sentence and constrained words with a special token to input them into the encoder of NMT.The key to the successful lexically-constrained NMT is the way to extract constraints from a target sentence of training data.We propose two extraction methods: proper-noun constraint and mistranslated-word constraint.These two methods consider the importance of words and fallibility of NMT, respectively.The evaluation results demonstrate the effectiveness of our lexical-constraint method.