# A Bilingual Parallel Corpus with Discourse Annotations

arXiv: https://arxiv.org/abs/2210.14667 · HTML: https://mlsanity.com/paper/2210.14667/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation](https://mlsanity.com/paper/1808.07048/) (2018)
- [Evaluating Discourse Phenomena in Neural Machine Translation](https://mlsanity.com/paper/1711.00513/) (2017)
- [Document-Level Neural Machine Translation with Hierarchical Attention Networks](https://mlsanity.com/paper/1809.01576/) (2018)
- [Improving the Transformer Translation Model with Document-Level Context](https://mlsanity.com/paper/1810.03581/) (2018)
- [BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation](https://mlsanity.com/paper/2103.11878/) (2021)
- [Exploiting Cross-Sentence Context for Neural Machine Translation](https://mlsanity.com/paper/1704.04347/) (2017)
- [Document Context Neural Machine Translation with Memory Networks](https://mlsanity.com/paper/1711.03688/) (2017)
- [Context-Aware Monolingual Repair for Neural Machine Translation](https://mlsanity.com/paper/1909.01383/) (2019)
- [A Challenge Set Approach to Evaluating Machine Translation](https://mlsanity.com/paper/1704.07431/) (2017)
- [A Set of Recommendations for Assessing Human-Machine Parity in Language Translation](https://mlsanity.com/paper/2004.01694/) (2020)
- [On the Evaluation of Machine Translation for Terminology Consistency](https://mlsanity.com/paper/2106.11891/) (2021)
- [Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point](https://mlsanity.com/paper/1808.04164/) (2018)

## Built on by (most cited first, 4 of 4)

- [DocHPLT: A Massively Multilingual Document-Level Translation Dataset](https://mlsanity.com/paper/2508.13079/) (2025)
- [Challenges in Context-Aware Neural Machine Translation](https://mlsanity.com/paper/2305.13751/) (2023)
- [DITING: A Multi-Agent Evaluation Framework for Benchmarking Web Novel Translation](https://mlsanity.com/paper/2510.09116/) (2025)
- [How Far Can Machine Translation Quality Take You? Extrinsic Discourse Evaluation in Goal-Oriented Setups](https://mlsanity.com/paper/2606.16596/) (2026)

## 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.