# Rethinking Document-level Neural Machine Translation

arXiv: https://arxiv.org/abs/2010.08961 · HTML: https://mlsanity.com/paper/2010.08961/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2020.

## Built on by (most cited first, 30 of 32)

- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Document-Level Machine Translation with Large Language Models](https://mlsanity.com/paper/2304.02210/) (2023)
- [Uncertainty in Natural Language Generation: From Theory to Applications](https://mlsanity.com/paper/2307.15703/) (2023)
- [(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts](https://mlsanity.com/paper/2405.11804/) (2024)
- [A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks](https://mlsanity.com/paper/2407.12994/) (2024)
- [Speech Translation with Large Language Models: An Industrial Practice](https://mlsanity.com/paper/2312.13585/) (2023)
- [Escaping the sentence-level paradigm in machine translation](https://mlsanity.com/paper/2304.12959/) (2023)
- [DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory](https://mlsanity.com/paper/2410.08143/) (2024)
- [Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation](https://mlsanity.com/paper/2302.08079/) (2023)
- [Enhancing Document-level Translation of Large Language Model via Translation Mixed-instructions](https://mlsanity.com/paper/2401.08088/) (2024)
- [Multilingual Contextualization of Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2504.12140/) (2025)
- [Training and Meta-Evaluating Machine Translation Evaluation Metrics at the Paragraph Level](https://mlsanity.com/paper/2308.13506/) (2023)
- [Improving Long Context Document-Level Machine Translation](https://mlsanity.com/paper/2306.05183/) (2023)
- [Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts](https://mlsanity.com/paper/2209.11409/) (2022)
- [Focused Concatenation for Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2210.13388/) (2022)
- [Controlling Styles in Neural Machine Translation with Activation Prompt](https://mlsanity.com/paper/2212.08909/) (2022)
- [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)
- [P-Transformer: Towards Better Document-to-Document Neural Machine Translation](https://mlsanity.com/paper/2212.05830/) (2022)
- [Target-Side Augmentation for Document-Level Machine Translation](https://mlsanity.com/paper/2305.04505/) (2023)
- [Challenges in Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2305.13751/) (2023)
- [Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models](https://mlsanity.com/paper/2212.07752/) (2022)
- [Context-Aware Machine Translation with Source Coreference Explanation](https://mlsanity.com/paper/2404.19505/) (2024)
- [Towards Chapter-to-Chapter Context-Aware Literary Translation via Large Language Models](https://mlsanity.com/paper/2407.08978/) (2024)
- [Speech Translation Refinement using Large Language Models](https://mlsanity.com/paper/2501.15090/) (2025)
- [Non-Autoregressive Document-Level Machine Translation](https://mlsanity.com/paper/2305.12878/) (2023)
- [A Case Study on Context Encoding in Multi-Encoder based Document-Level Neural Machine Translation](https://mlsanity.com/paper/2308.06063/) (2023)
- [Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning](https://mlsanity.com/paper/2401.15043/) (2024)
- [Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2209.13940/) (2022)

## Abstract (arXiv)

This paper does not aim at introducing a novel model for document-level neural machine translation. Instead, we head back to the original Transformer model and hope to answer the following question: Is the capacity of current models strong enough for document-level translation? Interestingly, we observe that the original Transformer with appropriate training techniques can achieve strong results for document translation, even with a length of 2000 words. We evaluate this model and several recent approaches on nine document-level datasets and two sentence-level datasets across six languages. Experiments show that document-level Transformer models outperforms sentence-level ones and many previous methods in a comprehensive set of metrics, including BLEU, four lexical indices, three newly proposed assistant linguistic indicators, and human evaluation.