# Using Whole Document Context in Neural Machine Translation

arXiv: https://arxiv.org/abs/1910.07481 · HTML: https://mlsanity.com/paper/1910.07481/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [Document-Level Machine Translation with Large Language Models](https://mlsanity.com/paper/2304.02210/) (2023)
- [Adapting Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2401.06468/) (2024)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning](https://mlsanity.com/paper/2406.07081/) (2024)
- [Multilingual Contextualization of Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2504.12140/) (2025)
- [Improving Long Context Document-Level Machine Translation](https://mlsanity.com/paper/2306.05183/) (2023)
- [Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context](https://mlsanity.com/paper/2311.15507/) (2023)
- [Revisiting Context Choices for Context-aware Machine Translation](https://mlsanity.com/paper/2109.02995/) (2021)
- [Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decoding](https://mlsanity.com/paper/2510.06866/) (2025)

## Abstract (arXiv)

In Machine Translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a simple yet promising approach to add contextual information in Neural Machine Translation. We present a method to add source context that capture the whole document with accurate boundaries, taking every word into account. We provide this additional information to a Transformer model and study the impact of our method on three language pairs. The proposed approach obtains promising results in the English-German, English-French and French-English document-level translation tasks. We observe interesting cross-sentential behaviors where the model learns to use document-level information to improve translation coherence.