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