Combining Input Augmentation and Constrained Decoding for Lexically-Constrained Neural Machine Translation

Abstract

Lexically constrained machine translation is a task wherein the translation model is required to output translated sentences that contain all specified phrase constraints. In this paper, we propose a method for improving the efficiency of lexically-constrained decoding by extending the input sequence of the model. The results of experiments performed on En↔Ja indicate that the proposed method achieves higher translation accuracy with less computational cost than do the conventional methods. Furthermore, we propose a method for automatically extracting noisy lexical constraints by using the lexical constraint machine translation method. Experiments on Ja→En show that the proposed method can achieve a higher level of accuracy than do general machine translation methods.