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Chinese Machine Reading Comprehension Datasets

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Section Description
Chinese Reading Comprehension Datasets Describe public Chinese RC datasets
State-of-the-art Systems State-of-the-art systems and results
Chinese Reading Comprehension Evaluations and Competitions Introductions to Chinese RC competitions

Chinese Reading Comprehension Datasets

Here I list several Chinese reading comprehension datasets that are PUBLICLY available (with appropriate technical report or paper). If I missed something, feel free to inform me. Unless indicated, the datasets are in simplified Chinese.

Dataset Genre Query Type Answer Type Document # Query # Download
People Daily & Children's Fairy Tale [1] news & tale Cloze word 28K 100K link
WebQA [2] Web User log entity - 42K link
CMRC 2017 [3] news Cloze & Query word - 364K link
DuReader [4] Web User log free form 1M 200K link
CMRC 2018 [5] Wiki Query Span - 18K link
DRCD [6](tranditional Chinese) Wiki Query Span - 34K link
C^3 [7] mixed Query choice 14K 24K link
CMRC 2019 [8] Story cloze Sentence 1K 100K link
ChID [9] varies cloze idiom 580K 729K link

[1] (Cui et al., 2016) Consensus Attention-based Neural Networks for Chinese Reading Comprehension. In COLING 2016. https://aclanthology.info/papers/C16-1167/c16-1167

[2] (Li et al., 2016) Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering. In arXiv. https://arxiv.org/abs/1607.06275

[3] (Cui et al., 2018) Dataset for the First Evaluation on Chinese Machine Reading Comprehension. In LREC 2018. http://www.lrec-conf.org/proceedings/lrec2018/summaries/32.html

[4] (He et al., 2018) DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications. In ACL 2018 MRQA Workshop. https://aclanthology.info/papers/W18-2605/w18-2605

[5] (Cui et al., 2018) A Span-Extraction Dataset for Chinese Machine Reading Comprehension. In arXiv. https://arxiv.org/abs/1810.07366

[6] (Shao et al., 2018) DRCD: a Chinese Machine Reading Comprehension Dataset. In arXiv. https://arxiv.org/abs/1806.00920

[7] (Sun et al., 2019) Probing Prior Knowledge Needed in Challenging Chinese Machine Reading Comprehension. https://arxiv.org/abs/1904.09679

[8] (Cui et al., 2019) https://github.com/ymcui/cmrc2019

[9] (Zheng et al., 2019) ChID: A Large-scale Chinese IDiom Dataset for Cloze Test. https://aclweb.org/anthology/papers/P/P19/P19-1075/

State-of-the-art Systems

Here I list several state-of-the-art systems (published / unpublished) for these datasets. There is a big chance that I missed something. So feel free to inform me new entries on Issue tab.

People Daily & Children's Fairy Tale

System PD-DEV PD-TEST CFT-TEST-AUTO CFT-TEST-HUMAN Note
SAW Reader (Zhang et al., 2018) 72.8 75.1 - 43.8 -
CAW Reader (Zhang et al., 2018) 69.4 70.5 - 39.7 -
CAS Reader (Cui et al., 2016) 65.2 68.1 41.3 35.0 -
AS Reader (Cui et al., 2016) 64.1 67.2 40.9 33.1 -

CMRC 2017

Leaderboard: https://hfl-rc.github.io/cmrc2017/leaderboard/

Cloze Track

System DEV TEST Note
6ESTATES PTE LTD (ensemble) 81.85 81.90 -
SJTU BCMI-NLP (ensemble) 78.35 80.67 -
YunSiChuangZhi (ensemble) 79.20 80.27 -
SAW Reader (Zhang et al., 2018) 78.95 78.80 -
CAW Reader (Zhang et al., 2018) 77.95 78.50 -
Word + Char + BPE-FRQ (Zhang et al., 2018) 79.05 78.83 -

User Query Track

System DEV TEST Note
ECNU (ensemble) 90.45 69.53 -
SXU-3 (single model) 47.80 49.07 -
ZZU (single model) 31.10 32.53 -

DuReader

Leaderboard: http://ai.baidu.com/broad/leaderboard?dataset=dureader

System ROUGE-L BLEU-4 Note
AliReader 63.48 61.54 -
NI-Reader (ensemble) 63.38 59.23 -
mrc_try_mingyan (single model) 62.20 59.72 -
(Yan et al., 2018) 50.71 49.39 -
(Li et al., 2018) 44.95 42.68 -
(Wang et al., 2018) 44.18 40.97 -
(Xu et al., 2018) 39.60 34.76 -
Match-LSTM (He et al., 2018) 39.2 31.9 -
BiDAF (He et al., 2018) 39.0 31.8 -

CMRC 2018

Leaderboard: https://hfl-rc.github.io/cmrc2018/open_challenge/

System DEV-EM DEV-F1 TEST-EM TEST-F1 CHALLENGE-EM CHALLENGE-F1 Note
P-Reader (single model) 59.894 81.499 65.189 84.386 15.079 39.583 -
GM-Reader (ensemble) 58.931 80.069 64.045 83.046 15.675 37.315 -
MCA-Reader (ensemble) 66.698 85.538 71.175 88.090 15.476 37.104 -
Z-Reader (single model) 79.776 92.696 74.178 88.145 13.889 37.422 -
SRC->DS(±) (Yang et al., 2019) 49.2 65.4 - - - - -

More detailed results can be obtained in CMRC 2018 Overview. Note that, some of the submission are using development set for training as well.

DRCD

System DEV-EM DEV-F1 TEST-EM TEST-EM Note
SRC + DS(±) (Yang et al., 2019) 55.4 67.7 - - -
r-net (single model) - - 29.1 44.4 -

C^3

System DEV-1A TEST-1A DEV-1B TEST-1B DEV-2A TEST-2A DEV-2B TEST-2B Note
BERT_CN (Sun et al., 2019) 63.0 62.6 62.3 62.1 36.7 26.2 34.7 31.3 -

Chinese Reading Comprehension Evaluations and Competitions

Along with the release of these datasets, there are also several Chinese Reading Comprehension evaluation workshops or competitions which further accelerate the research on this topic.

  1. The First Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2017)
    Host: CIPS-CL, Joint Laboratory of HIT and iFLYTEK Research (HFL), iFLYTEK Co. Ltd
    Competition Type: Cloze-style RC, User Query RC
  1. The Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018)
    Host: CIPS-CL, Joint Laboratory of HIT and iFLYTEK Research (HFL), iFLYTEK Co. Ltd
    Competition Type: Span-Extraction RC
  1. 2018 NLP Challenge on Machine Reading Comprehension
    Host: CCF, CIPSC, Baidu Inc.
    Competition Type: Open-Domain RC
  1. CIPS-SOGOU QA Competition
    Host: CIPSC, SOGOU
    Competition Type: Factoid QA, Non-Factoid QA
  1. The Third Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2019)
    Host: CIPS-CL, Joint Laboratory of HIT and iFLYTEK Research (HFL), iFLYTEK Co. Ltd
    Competition Type: Sentence Cloze
  1. 2019 NLP Language and Intelligence Challenge
    Host: CCF, CIPSC, Baidu Inc.
    Competition Type: Open-Domain RC
  1. Chinese Idiom Understanding Contest
    Host: CCF, Tsinghua University
    Competition Type: Cloze Test

Contact

For any problems, please leave a message in the Github Issues.

Disclaimer

Any subjective comments in this repository only represents the idea of the owner (ymcui), and does not represent the claims of any organizations.

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