  Context-aware neural machine translation (NMT) incorporates contextual
information of surrounding texts, that can improve the translation quality of
document-level machine translation. Many existing works on context-aware NMT
have focused on developing new model architectures for incorporating additional
contexts and have shown some promising results. However, most existing works
rely on cross-entropy loss, resulting in limited use of contextual information.
In this paper, we propose CorefCL, a novel data augmentation and contrastive
learning scheme based on coreference between the source and contextual
sentences. By corrupting automatically detected coreference mentions in the
contextual sentence, CorefCL can train the model to be sensitive to coreference
inconsistency. We experimented with our method on common context-aware NMT
models and two document-level translation tasks. In the experiments, our method
consistently improved BLEU of compared models on English-German and
English-Korean tasks. We also show that our method significantly improves
coreference resolution in the English-German contrastive test suite.
