Controlling Japanese Machine Translation Output by Using JLPT Vocabulary Levels
Alberto Poncelas, Ohnmar Htun · 2022
In Neural Machine Translation (NMT) systems, there is generally little control over the lexicon of the output.Consequently, the translated output may be too difficult for certain audiences.For example, for people with limited knowledge of the language, vocabulary is a major impediment to understanding a text.In this work, we build a complexitycontrollable NMT for English-to-Japanese translations.More particularly, we aim to modulate the difficulty of the translation in terms of not only the vocabulary but also the use of kanji.For achieving this, we follow a sentencetagging approach to influence the output.