A Simple and Fast Strategy for Handling Rare Words in Neural Machine Translation

AbstractNeural Machine Translation (NMT) has currently obtained state-of-the-art in machine translation systems. However, dealing with rare words is still a big challenge in translation systems. The rare words are often translated using a manual dictionary or copied from the source to the target with original words. In this paper, we propose a simple and fast strategy for integrating constraints during the training and decoding process to improve the translation of rare words. The effectiveness of our proposal is demonstrated in both high and low-resource translation tasks, including the language pairs: English → Vietnamese, Chinese → Vietnamese, Khmer → Vietnamese, and Lao → Vietnamese. We show the improvements of up to +1.8 BLEU scores over the baseline systems.