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arXiv · 1903.04715

Context-Aware Learning for Neural Machine Translation

Sébastien Jean·Kyunghyun Cho
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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.

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Sébastien Jean, Kyunghyun Cho. 2019-03-12. Context-Aware Learning for Neural Machine Translation. https://arxiv.org/abs/1903.04715

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