Chinese Machine Reading Comprehension Datasets

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Content

| 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 Consensus Attention-based Neural Networks for Chinese Reading Comprehension. In COLING 2016. https://aclanthology.info/papers/C16-1167/c16-1167

2 Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering. In arXiv. https://arxiv.org/abs/1607.06275

3 Dataset for the First Evaluation on Chinese Machine Reading Comprehension. In LREC 2018. http://www.lrec-conf.org/proceedings/lrec2018/summaries/32.html

4 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 A Span-Extraction Dataset for Chinese Machine Reading Comprehension. In arXiv. https://arxiv.org/abs/1810.07366

6 DRCD: a Chinese Machine Reading Comprehension Dataset. In arXiv. https://arxiv.org/abs/1806.00920

7 Probing Prior Knowledge Needed in Challenging Chinese Machine Reading Comprehension. https://arxiv.org/abs/1904.09679

8 https://github.com/ymcui/cmrc2019

9 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

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 | - | | mrctrymingyan (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

2. 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

3. 2018 NLP Challenge on Machine Reading Comprehension
   Host: CCF, CIPSC, Baidu Inc.
   Competition Type: Open-Domain RC

4. CIPS-SOGOU QA Competition
   Host: CIPSC, SOGOU
   Competition Type: Factoid QA, Non-Factoid QA

5. 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

6. 2019 NLP Language and Intelligence Challenge
   Host: CCF, CIPSC, Baidu Inc.
   Competition Type: Open-Domain RC

7. Chinese Idiom Understanding Contest
   Host: CCF, Tsinghua University
   Competition Type: Cloze Test

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