Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach
Zonghan Yang, Yong Cheng, Yang Liu, Maosong Sun · 2019
While neural machine translation (NMT) has achieved remarkable success, NMT systems are prone to make word omission errors.In this work, we propose a contrastive learning approach to reducing word omission errors in NMT.The basic idea is to enable the NMT model to assign a higher probability to a ground-truth translation and a lower probability to an erroneous translation, which is automatically constructed from the ground-truth translation by omitting words.We design different types of negative examples depending on the number of omitted words, word frequency, and part of speech.Experiments on Chinese-to-English, German-to-English, and Russian-to-English translation tasks show that our approach is effective in reducing word omission errors and achieves better translation performance than three baseline methods.