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.
@article{arxiv.1903.04715,
title = {Context-Aware Learning for Neural Machine Translation},
author = {Sébastien Jean and Kyunghyun Cho},
journal= {arXiv preprint arXiv:1903.04715},
year = {2019}
}