---
title: "文档级神经机器翻译综述 — 吕星林 2024"
description: "Sentence-level neural machine translation (NMT) handles each sentence of a document separately."
authors: ["吕星林", "李军辉", "陶仕敏", "杨浩", "张民"]
year: 2024
venue: "软件学报 (Journal of Software)"
source: "https://jos.org.cn/jos/article/abstract/7217"
doi: "10.13328/j.cnki.jos.007217"
licence: "Free access (jos.org.cn), no open licence stated — readonly; © 中国科学院软件研究所 / 软件学报"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/lv-2024-document-level-neural/"
---
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# 文档级神经机器翻译综述

**Authors:** 吕星林, 李军辉, 陶仕敏, 杨浩, 张民 · **Year:** 2024 · **Venue:** 软件学报 (Journal of Software) · **Licence:** Free access (jos.org.cn), no open licence stated — readonly; © 中国科学院软件研究所 / 软件学报

**Links:** [Publisher's page](https://doi.org/10.13328/j.cnki.jos.007217) · [PDF](https://www.jos.org.cn//jos/article/pdf/7217) · [DOI 10.13328/j.cnki.jos.007217](https://doi.org/10.13328/j.cnki.jos.007217)

## Summary
Sentence-level neural machine translation (NMT) handles each sentence of a document separately. As a result, it often produces discourse errors such as inconsistent tense across sentences, wrong pronoun or number agreement, and weak cohesion. The authors, from Soochow University and Huawei's translation center, survey document-level NMT (DNMT), meaning methods that use cross-sentence context from the source text, earlier target translations, or both.

The survey first defines DNMT formally as sentence-level encoder-decoder translation conditioned on extra document context. It then covers:
- shared tasks and the most widely used document-level corpora, with statistics;
- evaluation metrics designed for discourse phenomena;
- mainstream model families, from early RNN context encoders and caches to Transformer-based multi-encoder designs;
- approaches built on pretrained models.

As a review, its main output is an organized taxonomy and resource overview, not a new system. It closes with open problems and future directions. This makes it a useful entry point for anyone building or evaluating context-aware translation, including for lower-resource language pairs.

## Key points
- **Motivating example.** A Chinese→English Google Translate output from February 2023 switches between present and past tense across sentences. It also renders plural "tadpoles" as singular, which breaks the matching pronouns.
- **Shared tasks.**
  - IWSLT has offered document-level TED data since 2012, but scores it with sentence-level metrics (BLEU, METEOR, TER).
  - WMT added document-level corpora in 2019.
  - WAT has included cross-sentence tasks with human evaluation since 2014.
- **Common corpora.**
  - Zh→En: LDC/NIST, about 41k documents and 940k sentences.
  - En→De: IWSLT2017, about 1.7k documents and 206k sentences; News-Commentary v11; Europarl v7, about 118k documents and 1.7M sentences.
  - OpenSubtitles2018 is also widely used, but it has no document boundaries.
- **Domain differences.** Subtitles are rich in ellipsis and zero anaphora. TED talks are long and contain distant coreference. News shows a balanced mix of phenomena.
- **Discourse metrics surveyed.**
  - Pronoun translation: APT, AutoPRF, CRC.
  - Lexical cohesion and consistency: LC/RC, LTCR, HHI.
  - Connectives: ACT.
  - Composite metrics: BLONDE, TCP.
  - Document-level BLEU is common but largely misses these phenomena.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Natural language processing](https://md.research.elamur.ai/free/shelves/nlp/)

Topics: [Discourse analysis](https://md.research.elamur.ai/free/topics/discourse-analysis/) · [Machine translation](https://md.research.elamur.ai/free/topics/machine-translation/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{2024,
  title = {{文档级神经机器翻译综述}},
  author = {吕星林 and 李军辉 and 陶仕敏 and 杨浩 and 张民},
  journal = {软件学报 (Journal of Software)},
  year = {2024},
  doi = {10.13328/j.cnki.jos.007217},
  url = {https://doi.org/10.13328/j.cnki.jos.007217}
}
```

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