# Context-Aware Learning for Neural Machine Translation

arXiv: https://arxiv.org/abs/1903.04715 · HTML: https://mlsanity.com/paper/1903.04715/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Measuring and Increasing Context Usage in Context-Aware Machine Translation](https://mlsanity.com/paper/2105.03482/) (2021)
- [HanoiT: Enhancing Context-aware Translation via Selective Context](https://mlsanity.com/paper/2301.06825/) (2023)
- [Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust](https://mlsanity.com/paper/2205.06920/) (2022)

## Abstract (arXiv)

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.