Simultaneous Neural Machine Translation with Constituent Label Prediction

Abstract

Simultaneous translation is a task in which translation begins before the speaker has finished speaking, so it is important to decide when to start the translation process. However, deciding whether to read more input words or start to translate is difficult for language pairs with different word orders such as English and Japanese. Motivated by the concept of prereordering, we propose a couple of simple decision rules using the label of the next constituent predicted by incremental constituent label prediction. In experiments on Englishto-Japanese simultaneous translation, the proposed method outperformed baselines in the quality-latency trade-off.