# A Survey on Document-level Neural Machine Translation: Methods and Evaluation

arXiv: https://arxiv.org/abs/1912.08494 · HTML: https://mlsanity.com/paper/1912.08494/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 12 of 12)

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Measuring and Increasing Context Usage in Context-Aware Machine Translation](https://mlsanity.com/paper/2105.03482/) (2021)
- [Escaping the sentence-level paradigm in machine translation](https://mlsanity.com/paper/2304.12959/) (2023)
- [Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models](https://mlsanity.com/paper/2103.17151/) (2021)
- [A baseline revisited: Pushing the limits of multi-segment models for context-aware translation](https://mlsanity.com/paper/2210.10906/) (2022)
- [SLIDE: Reference-free Evaluation for Machine Translation using a Sliding Document Window](https://mlsanity.com/paper/2309.08832/) (2023)
- [Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language](https://mlsanity.com/paper/2311.11142/) (2023)
- [From Simultaneous to Streaming Machine Translation by Leveraging Streaming History](https://mlsanity.com/paper/2203.02459/) (2022)
- [HanoiT: Enhancing Context-aware Translation via Selective Context](https://mlsanity.com/paper/2301.06825/) (2023)
- [Evaluating Optimal Reference Translations](https://mlsanity.com/paper/2311.16787/) (2023)
- [Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages](https://mlsanity.com/paper/2305.03873/) (2023)
- [LLMs for Low-Resource Dialect Translation Using Context-Aware Prompting: A Case Study on Sylheti](https://mlsanity.com/paper/2511.21761/) (2025)

## Abstract (arXiv)

Machine translation (MT) is an important task in natural language processing (NLP) as it automates the translation process and reduces the reliance on human translators. With the resurgence of neural networks, the translation quality surpasses that of the translations obtained using statistical techniques for most language-pairs. Up until a few years ago, almost all of the neural translation models translated sentences independently, without incorporating the wider document-context and inter-dependencies among the sentences. The aim of this survey paper is to highlight the major works that have been undertaken in the space of document-level machine translation after the neural revolution, so that researchers can recognise the current state and future directions of this field. We provide an organisation of the literature based on novelties in modelling and architectures as well as training and decoding strategies. In addition, we cover evaluation strategies that have been introduced to account for the improvements in document MT, including automatic metrics and discourse-targeted test sets. We conclude by presenting possible avenues for future exploration in this research field.