Context-Aware Learning for Neural Machine Translation

Abstract

Interest in larger-context neural machine translation, including document-level and multi-modal translation, has been growing. Multiple works have proposed new network architectures or evaluation schemes, but potentially helpful context is still sometimes ignored by larger-context translation models. In this paper, we propose a novel learning algorithm that explicitly encourages a neural translation model to take into account additional context using a multilevel pair-wise ranking loss. We evaluate the proposed learning algorithm with a transformer-based larger-context translation system on document-level translation. By comparing performance using actual and random contexts, we show that a model trained with the proposed algorithm is more sensitive to the additional context.

References in corpus (7)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Convolutional Sequence to Sequence Learning
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• Exploiting Cross-Sentence Context for Neural Machine Translation
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Cited by in corpus (4)

• A Survey on Document-level Neural Machine Translation: Methods and Evaluation
• SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
• Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning
• Analysing Coreference in Transformer Outputs