mlsanityData to 7 Oct 2026

A Bilingual Parallel Corpus with Discourse Annotations

arXiv 2210.14667 ↗First posted 26 Oct 2022cs.CLcited by 3

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Abstract (arXiv)

Machine translation (MT) has almost achieved human parity at sentence-level translation. In response, the MT community has, in part, shifted its focus to document-level translation. However, the development of document-level MT systems is hampered by the lack of parallel document corpora. This paper describes BWB, a large parallel corpus first introduced in Jiang et al. (2022), along with an annotated test set. The BWB corpus consists of Chinese novels translated by experts into English, and the annotated test set is designed to probe the ability of machine translation systems to model various discourse phenomena. Our resource is freely available, and we hope it will serve as a guide and inspiration for more work in document-level machine translation.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 55 references that are in the map. First posted on arXiv 26 Oct 2022.

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16 of 55 references.

References · 16

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4 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.

  1. 2025DocHPLT: A Massively Multilingual Document-Level Translation Datasetcited by 7
  2. 2023Challenges in Context-Aware Neural Machine Translationcited by 5
  3. 2025DITING: A Multi-Agent Evaluation Framework for Benchmarking Web Novel Translationcited by 4