Google Research

Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach

ACL (2019)

Abstract

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.

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work