# Context in Neural Machine Translation: A Review of Models and Evaluations

arXiv: https://arxiv.org/abs/1901.09115 · HTML: https://mlsanity.com/paper/1901.09115/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2019.

## Built on by (most cited first, 7 of 7)

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models](https://mlsanity.com/paper/2103.17151/) (2021)
- [Investigating Length Issues in Document-level Machine Translation](https://mlsanity.com/paper/2412.17592/) (2024)
- [Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context](https://mlsanity.com/paper/2311.15507/) (2023)
- [Gradable ChatGPT Translation Evaluation](https://mlsanity.com/paper/2401.09984/) (2024)
- [Chain-of-Thought Reasoning Improves Context-Aware Translation with Large Language Models](https://mlsanity.com/paper/2510.18077/) (2025)
- [SMDDH: Singleton Mention detection using Deep Learning in Hindi Text](https://mlsanity.com/paper/2301.09361/) (2023)

## Abstract (arXiv)

This review paper discusses how context has been used in neural machine translation (NMT) in the past two years (2017-2018). Starting with a brief retrospect on the rapid evolution of NMT models, the paper then reviews studies that evaluate NMT output from various perspectives, with emphasis on those analyzing limitations of the translation of contextual phenomena. In a subsequent version, the paper will then present the main methods that were proposed to leverage context for improving translation quality, and distinguishes methods that aim to improve the translation of specific phenomena from those that consider a wider unstructured context.