# When and Why is Document-level Context Useful in Neural Machine Translation?

arXiv: https://arxiv.org/abs/1910.00294 · HTML: https://mlsanity.com/paper/1910.00294/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 30 of 45)

- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metric](https://mlsanity.com/paper/2209.13654/) (2022)
- [Measuring and Increasing Context Usage in Context-Aware Machine Translation](https://mlsanity.com/paper/2105.03482/) (2021)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Quantifying Context Mixing in Transformers](https://mlsanity.com/paper/2301.12971/) (2023)
- [MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation](https://mlsanity.com/paper/2211.01355/) (2022)
- [When Does Translation Require Context? A Data-driven, Multilingual Exploration](https://mlsanity.com/paper/2109.07446/) (2021)
- [DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory](https://mlsanity.com/paper/2410.08143/) (2024)
- [Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges](https://mlsanity.com/paper/2301.10075/) (2023)
- [Do Context-Aware Translation Models Pay the Right Attention?](https://mlsanity.com/paper/2105.06977/) (2021)
- [A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation](https://mlsanity.com/paper/2310.12127/) (2023)
- [How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?](https://mlsanity.com/paper/2412.18495/) (2024)
- [Diverse Pretrained Context Encodings Improve Document Translation](https://mlsanity.com/paper/2106.03717/) (2021)
- [Learning to Attribute with Attention](https://mlsanity.com/paper/2504.13752/) (2025)
- [Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models](https://mlsanity.com/paper/2103.17151/) (2021)
- [Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution](https://mlsanity.com/paper/2105.01691/) (2021)
- [Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information](https://mlsanity.com/paper/2109.05712/) (2021)
- [On Measuring Context Utilization in Document-Level MT Systems](https://mlsanity.com/paper/2402.01404/) (2024)
- [Improving Long Context Document-Level Machine Translation](https://mlsanity.com/paper/2306.05183/) (2023)
- [Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents](https://mlsanity.com/paper/2109.10341/) (2021)
- [Focused Concatenation for Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2210.13388/) (2022)
- [Quantifying the Plausibility of Context Reliance in Neural Machine Translation](https://mlsanity.com/paper/2310.01188/) (2023)
- [Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translation](https://mlsanity.com/paper/2505.08546/) (2025)
- [DocHPLT: A Massively Multilingual Document-Level Translation Dataset](https://mlsanity.com/paper/2508.13079/) (2025)
- [Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenation](https://mlsanity.com/paper/2302.06459/) (2023)
- [FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection](https://mlsanity.com/paper/2309.10770/) (2023)
- [Dodging the Data Bottleneck: Automatic Subtitling with Automatically Segmented ST Corpora](https://mlsanity.com/paper/2209.10608/) (2022)
- [Challenges in Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2305.13751/) (2023)
- [TRAMS: Training-free Memory Selection for Long-range Language Modeling](https://mlsanity.com/paper/2310.15494/) (2023)
- [Towards Personalised and Document-level Machine Translation of Dialogue](https://mlsanity.com/paper/2102.10979/) (2021)

## Abstract (arXiv)

Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT. We show that most of the improvements are not interpretable as utilizing the context. We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT.