---
title: "A Span-Extraction Dataset for Chinese Machine — Cui 2018"
description: "Most machine reading comprehension (MRC) benchmarks are in English, so it is unclear whether models that beat humans on SQuAD perform as well in other…"
authors: ["Yiming Cui", "Ting Liu", "Wanxiang Che", "Li Xiao", "Zhipeng Chen", "Wentao Ma", "Shijin Wang", "Guoping Hu"]
year: 2018
venue: "arXiv:1810.07366 · EMNLP 2019 5886-5891"
source: "https://arxiv.org/abs/1810.07366"
doi: "10.18653/v1/d19-1600"
arxiv: "1810.07366"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/cui-2018-span-extraction-dataset-chinese/"
---
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# A Span-Extraction Dataset for Chinese Machine Reading Comprehension

**Authors:** [Yiming Cui](https://md.research.elamur.ai/free/en/authors/yiming-cui/), [Ting Liu](https://md.research.elamur.ai/free/en/authors/ting-liu/), [Wanxiang Che](https://md.research.elamur.ai/free/en/authors/wanxiang-che/), Li Xiao, [Zhipeng Chen](https://md.research.elamur.ai/free/en/authors/zhipeng-chen/), Wentao Ma, [Shijin Wang](https://md.research.elamur.ai/free/en/authors/shijin-wang/), [Guoping Hu](https://md.research.elamur.ai/free/en/authors/guoping-hu/) · **Year:** 2018 · **Venue:** arXiv:1810.07366 · EMNLP 2019 5886-5891 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://doi.org/10.18653/v1/d19-1600) · [PDF](https://arxiv.org/pdf/1810.07366) · [DOI 10.18653/v1/d19-1600](https://doi.org/10.18653/v1/d19-1600) · [arXiv 1810.07366](https://arxiv.org/abs/1810.07366) · [OpenAlex](https://openalex.org/W2897356710)

**Citations:** 67 citations (openalex-snapshot · 2026-10-14) · [89](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Most machine reading comprehension (MRC) benchmarks are in English, so it is unclear whether models that beat humans on SQuAD perform as well in other languages. The authors address this by releasing CMRC 2018, a Chinese span-extraction dataset in the style of SQuAD. They took passages from the Chinese Wikipedia, normalized them to Simplified script, and filtered out unsuitable ones. Human experts then wrote roughly 19,000 questions. Annotators were asked to rephrase the passage text rather than copy it, and the evaluation sets have three independent reference answers per question. A separate challenge set targets questions that need reasoning over several sentences and include distractor entities. The authors adapt EM and F1 to Chinese by computing F1 at the character level using the longest common sequence. On the test set, competition systems and BERT baselines reach above 80 F1, but EM lags much further behind than on SQuAD. On the challenge set, every model stays below 50 F1, while humans score about 95. The dataset therefore adds language diversity to MRC and exposes weaknesses in multi-clue reasoning.

## Key points
- **Splits:** train 10,321, dev 3,351, test 4,895 and challenge 504 questions. Passages come from a Chinese Wikipedia dump dated 22 Jan 2018, are capped at 500 words (segmented with LTP), and were converted to Simplified script with OpenCC.
- **Estimated human performance:** computed by cross-validation over the three answers. It reaches 92.4 EM / 97.9 F1 on test and 90.4 EM / 95.2 F1 on challenge.
- **Best test system:** Z-Reader, a CMRC 2018 submission, scored 74.2 EM / 88.1 F1 on test. However, it got only about 13.9 EM on the challenge set.
- **BERT baselines:** BERT-base (Multi-lingual) reached 68.6 EM / 86.8 F1 on test and 18.6 EM / 43.8 F1 on challenge. Both BERT variants beat all participant systems on the challenge set.
- **Release:** the dataset was used for the CMRC 2018 evaluation workshop. Test and challenge sets remain hidden and are evaluated via CodaLab, and resources are at github.com/ymcui/cmrc2018.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

89 papers of our library cite it (of the 140884 whose bibliographies we read); 11 of them have a page here, 78 not yet.

- [Pre-Training with Whole Word Masking for Chinese BERT](https://md.research.elamur.ai/free/en/papers/cui-2019-pre-training-whole-word-masking/)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://md.research.elamur.ai/free/en/papers/cui-2020-revisiting-pre-trained-models-chinese/)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://md.research.elamur.ai/free/en/papers/lewis-2019-mlqa-evaluating-cross-lingual-extractive/)
- [Crosslingual Generalization through Multitask Finetuning](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/)
- [A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
- [BSC's Submission to the Instruction Following Track of IWSLT 2026](https://md.research.elamur.ai/free/en/papers/pareras-2026-bsc-s-submission-instruction-following/)
- [ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://md.research.elamur.ai/free/en/papers/sun-2019-ernie-2-0-continual/)
- [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://md.research.elamur.ai/free/en/papers/sun-2021-ernie-3-0-large/)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
- [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://md.research.elamur.ai/free/en/papers/xu-2020-clue-chinese-language-understanding/)
- [PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation](https://md.research.elamur.ai/free/en/papers/zeng-2021-pangu-alpha-large-scale/)

## Cite

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

```bibtex
@misc{cui2018span,
  title = {{A Span-Extraction Dataset for Chinese Machine Reading Comprehension}},
  author = {Cui, Yiming and Liu, Ting and Che, Wanxiang and Xiao, Li and Chen, Zhipeng and Ma, Wentao and Wang, Shijin and Hu, Guoping},
  year = {2018},
  doi = {10.18653/v1/d19-1600},
  eprint = {1810.07366},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/d19-1600}
}
```

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24 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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