mlsanityData to 7 Oct 2026

A Comparison of Approaches to Document-level Machine Translation

arXiv 2101.11040 ↗First posted 26 Jan 2021cs.CLcited by 7

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Abstract (arXiv)

Document-level machine translation conditions on surrounding sentences to produce coherent translations. There has been much recent work in this area with the introduction of custom model architectures and decoding algorithms. This paper presents a systematic comparison of selected approaches from the literature on two benchmarks for which document-level phenomena evaluation suites exist. We find that a simple method based purely on back-translating monolingual document-level data performs as well as much more elaborate alternatives, both in terms of document-level metrics as well as human evaluation.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 30 of its 40 references that are in the map. First posted on arXiv 26 Jan 2021.

Builds on

30 of 40 references.

References · 30

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18 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 1; 2025: 1.

7 papers.

  1. 2025LLMs Get Lost In Multi-Turn Conversationcited by 400
  2. 2024Investigating Length Issues in Document-level Machine Translationcited by 10
  3. 2021Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documentscited by 7
  4. 2022Focused Concatenation for Context-Aware Neural Machine Translationcited by 7
  5. 2023Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenationcited by 6
  6. 2023Challenges in Context-Aware Neural Machine Translationcited by 5
  7. 2022Modeling Context With Linear Attention for Scalable Document-Level Translationcited by 3