SQuAD2.0

The Stanford Question Answering Dataset

What is SQuAD?

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.

New SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 new, unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuAD2.0 is a challenging natural language understanding task for existing models, and we release SQuAD2.0 to the community as the successor to SQuAD1.1. We are optimistic that this new dataset will encourage the development of reading comprehension systems that know what they don't know.

SQuAD2.0 paper (Rajpurkar & Jia et al. '18)SQuAD1.0 paper (Rajpurkar et al. '16)

Getting Started

We've built a few resources to help you get started with the dataset.

Download a copy of the dataset (distributed under the CC BY-SA 4.0 license):

To evaluate your models, we have also made available the evaluation script we will use for official evaluation, along with a sample prediction file that the script will take as input. To run the evaluation, use python evaluate-v2.0.py <path_to_dev-v2.0> <path_to_predictions>.

Once you have a built a model that works to your expectations on the dev set, you submit it to get official scores on the dev and a hidden test set. To preserve the integrity of test results, we do not release the test set to the public. Instead, we require you to submit your model so that we can run it on the test set for you. Here's a tutorial walking you through official evaluation of your model:

Submission Tutorial

Because SQuAD is an ongoing effort, we expect the dataset to evolve.

To keep up to date with major changes to the dataset, please subscribe:

Have Questions?

Ask us questions at our google group or at pranavsr@stanford.edu and robinjia@stanford.edu.

Star

Leaderboard

SQuAD2.0 tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. How will your system compare to humans on this task?

RankModelEMF1
Human Performance

Stanford University

(Rajpurkar & Jia et al. '18)		86.83189.452

SQuAD1.1 Leaderboard

Upto May 2018 Since the release of SQuAD1.0, the community made rapid progress, with the best models rivaling human performance on the task. Here were the ExactMatch (EM) and F1 scores of the best models evaluated on the test set of v1.1.

RankModelEMF1
Human Performance

Stanford University

(Rajpurkar et al. '16)		82.30491.221

1

Mar 19, 2018		QANet (ensemble)

Google Brain & CMU

83.87789.737

2

May 10, 2018		MARS (ensemble)

YUANFUDAO research NLP

83.52089.612

3

Mar 06, 2018		QANet (ensemble)

Google Brain & CMU

82.74489.045

4

May 09, 2018		MARS (single model)

YUANFUDAO research NLP

82.58788.880

4

Jan 22, 2018		Hybrid AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

82.48289.281

4

Feb 19, 2018		Reinforced Mnemonic Reader + A2D (ensemble model)

Microsoft Research Asia & NUDT

82.84988.764

5

Jan 03, 2018		r-net+ (ensemble)

Microsoft Research Asia

82.65088.493

5

Feb 02, 2018		Reinforced Mnemonic Reader (ensemble model)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798		82.28388.533

5

Feb 27, 2018		QANet (single model)

Google Brain & CMU

82.20988.608

5

Jan 05, 2018		SLQA+ (ensemble)

Alibaba iDST NLP

82.44088.607

6

Dec 17, 2017		r-net (ensemble)

Microsoft Research Asia

http://aka.ms/rnet		82.13688.126

7

May 09, 2018		Reinforced Mnemonic Reader + A2D + DA (single model)

Microsoft Research Asia & NUDT

81.40188.122

7

Apr 23, 2018		r-net (single model)

Microsoft Research Asia

81.39188.170

7

Dec 22, 2017		AttentionReader+ (ensemble)

Tencent DPDAC NLP

81.79088.163

8

May 09, 2018		Reinforced Mnemonic Reader + A2D (single model)

Microsoft Research Asia & NUDT

81.53888.130

9

Nov 17, 2017		BiDAF + Self Attention + ELMo (ensemble)

Allen Institute for Artificial Intelligence

81.00387.432

9

Feb 27, 2018		QANet (single model)

Google Brain & CMU

80.92987.773

9

Apr 03, 2018		KACTEIL-MRC(GF-Net+) (ensemble model)

Kangwon National University, Natural Language Processing Lab.

81.49687.557

10

Feb 19, 2018		Reinforced Mnemonic Reader + A2D (single model)

Microsoft Research Asia & NUDT

80.91987.492

11

Apr 12, 2018		AVIQA+ (ensemble)

aviqa team

80.61587.311

11

Feb 12, 2018		Reinforced Mnemonic Reader + A2D (single model)

Microsoft Research Asia & NUDT

80.48987.454

12

Jan 13, 2018		SLQA+

single model

80.43687.021

13

Jan 12, 2018		EAZI+ (ensemble)

Yiwise NLP Group

80.42686.912

13

Jan 22, 2018		Hybrid AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

80.02787.288

13

Jan 04, 2018		{EAZI} (ensemble)

Yiwise NLP Group

80.43686.912

14

Mar 20, 2018		DNET (ensemble)

QA geeks

80.16486.721

15

Feb 12, 2018		BiDAF + Self Attention + ELMo + A2D (single model)

Microsoft Research Asia & NUDT

79.99686.711

16

Jan 29, 2018		Reinforced Mnemonic Reader (single model)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798		79.54586.654

16

Apr 10, 2018		Unnamed submission by null

80.02786.612

17

Jan 03, 2018		r-net+ (single model)

Microsoft Research Asia

79.90186.536

17

Feb 23, 2018		MAMCN+ (single model)

Samsung Research

79.69286.727

18

Dec 05, 2017		SAN (ensemble model)

Microsoft Business AI Solutions Team

https://arxiv.org/abs/1712.03556		79.60886.496

18

Dec 28, 2017		SLQA+ (single model)

Alibaba iDST NLP

79.19986.590

19

Oct 17, 2017		Interactive AoA Reader+ (ensemble)

Joint Laboratory of HIT and iFLYTEK

79.08386.450

20

Feb 01, 2018		Unnamed submission by null

78.99986.151

21

Oct 24, 2017		FusionNet (ensemble)

Microsoft Business AI Solutions Team

https://arxiv.org/abs/1711.07341		78.97886.016

22

Oct 22, 2017		DCN+ (ensemble)

Salesforce Research

https://arxiv.org/abs/1711.00106		78.85285.996

23

Nov 03, 2017		BiDAF + Self Attention + ELMo (single model)

Allen Institute for Artificial Intelligence

78.58085.833

23

Mar 29, 2018		KACTEIL-MRC(GF-Net+) (single model)

Kangwon National University, Natural Language Processing Lab.

78.66485.780

24

Nov 30, 2017		SLQA(ensemble)

Alibaba iDST NLP

78.32885.682

24

Jan 02, 2018		Conductor-net (ensemble)

CMU

https://arxiv.org/abs/1710.10504		78.43385.517

24

May 09, 2018		KakaoNet (single model)

Kakao NLP Team

78.40185.724

24

Mar 19, 2018		aviqa (ensemble)

aviqa team

78.49685.469

25

Jan 03, 2018		MEMEN (single model)

Zhejiang University

https://arxiv.org/abs/1707.09098		78.23485.344

25

Jan 29, 2018		test

single

78.08785.348

26

Jul 25, 2017		Interactive AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

77.84585.297

27

Jan 10, 2018		Unnamed submission by null

77.43685.130

27

Mar 20, 2018		DNET (single model)

QA geeks

77.64684.905

28

Jan 23, 2018		MARS (single model)

YUANFUDAO research NLP

76.85984.739

28

Apr 10, 2018		Unnamed submission by null

77.48984.735

28

Dec 06, 2017		AttentionReader+ (single)

Tencent DPDAC NLP

77.34284.925

29

Nov 06, 2017		Conductor-net (ensemble)

CMU

https://arxiv.org/abs/1710.10504		76.99684.630

30

May 14, 2018		VS^3-NET (single model)

Kangwon National University in South Korea

76.77584.491

30

Dec 19, 2017		FRC (single model)

in review

76.24084.599

30

Nov 01, 2017		SAN (single model)

Microsoft Business AI Solutions Team

https://arxiv.org/abs/1712.03556		76.82884.396

30

Dec 13, 2017		RaSoR + TR + LM (single model)

Tel-Aviv University

https://arxiv.org/abs/1712.03609		77.58384.163

30

Dec 21, 2017		Jenga (ensemble)

Facebook AI Research

77.23784.466

31

Oct 13, 2017		r-net (single model)

Microsoft Research Asia

http://aka.ms/rnet		76.46184.265

32

Oct 22, 2017		Conductor-net (ensemble)

CMU

76.14683.991

33

Sep 08, 2017		FusionNet (single model)

Microsoft Business AI Solutions team

https://arxiv.org/abs/1711.07341		75.96883.900

34

Oct 22, 2017		Interactive AoA Reader+ (single model)

Joint Laboratory of HIT and iFLYTEK

75.82183.843

34

Jul 14, 2017		smarnet (ensemble)

Eigen Technology & Zhejiang University

75.98983.475

35

Mar 15, 2018		AVIQA-v2 (single model)

aviqa team

75.92683.305

36

Aug 18, 2017		RaSoR + TR (single model)

Tel-Aviv University

https://arxiv.org/abs/1712.03609		75.78983.261

37

Oct 23, 2017		DCN+ (single model)

Salesforce Research

https://arxiv.org/abs/1711.00106		75.08783.081

38

Oct 31, 2017		SLQA (single model)

Alibaba iDST NLP

74.48982.815

38

Jul 10, 2017		DCN+ (single model)

Salesforce Research

https://arxiv.org/abs/1711.00106		74.86682.806

38

Nov 01, 2017		Mixed model (ensemble)

Sean

75.26582.769

39

Jan 02, 2018		Conductor-net (single model)

CMU

https://arxiv.org/abs/1710.10504		74.40582.742

39

Feb 06, 2018		Jenga (single model)

Facebook AI Research

74.37382.845

39

Nov 17, 2017		two-attention-self-attention (ensemble)

guotong1988

75.22382.716

39

May 21, 2017		MEMEN (ensemble)

Eigen Technology & Zhejiang University

https://arxiv.org/abs/1707.09098		75.37082.658

40

Mar 09, 2017		ReasoNet (ensemble)

MSR Redmond

https://arxiv.org/abs/1609.05284		75.03482.552

41

Feb 13, 2018		SSR-BiDAF

ensemble model

74.54182.477

42

Oct 27, 2017		Unnamed submission by null

74.48982.312

42

Jul 14, 2017		Mnemonic Reader (ensemble)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798		74.26882.371

43

Dec 23, 2017		S^3-Net (ensemble)

Kangwon National University in South Korea

74.12182.342

44

Jul 25, 2017		Interactive AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

73.63981.931

45

Dec 14, 2017		Jenga (single model)

Facebook AI Research

73.30381.754

45

Jul 06, 2017		SSAE (ensemble)

Tsinghua University

74.08081.665

46

Apr 22, 2017		SEDT+BiDAF (ensemble)

CMU

https://arxiv.org/abs/1703.00572		73.72381.530

46

Feb 22, 2017		BiDAF (ensemble)

Allen Institute for AI & University of Washington

https://arxiv.org/abs/1611.01603		73.74481.525

47

May 01, 2017		jNet (ensemble)

USTC & National Research Council Canada & York University

https://arxiv.org/abs/1703.04617		73.01081.517

47

Jan 24, 2017		Multi-Perspective Matching (ensemble)

IBM Research

https://arxiv.org/abs/1612.04211		73.76581.257

47

Nov 06, 2017		Conductor-net (single)

CMU

https://arxiv.org/abs/1710.10504		73.24081.933

47

Jul 29, 2017		SEDT (ensemble model)

CMU

https://arxiv.org/abs/1703.00572		74.09081.761

48

Sep 20, 2017		BiDAF + Self Attention (single model)

Allen Institute for Artificial Intelligence

https://arxiv.org/abs/1710.10723		72.13981.048

49

Dec 15, 2017		S^3-Net (single model)

Kangwon National University in South Korea

71.90881.023

49

Oct 22, 2017		Conductor-net (single)

CMU

72.59081.415

49

Nov 16, 2017		two-attention-self-attention (single model)

guotong1988

72.60081.011

49

Mar 13, 2018		KACTEIL-MRC(GF-Net) (single model)

Kangwon National University, Natural Language Processing Lab.

72.83180.622

49

Mar 29, 2018		KACTEIL-MRC(GF-Net) (single model)

Kangwon National University, Natural Language Processing Lab.

72.83180.622

49

Apr 12, 2017		T-gating (ensemble)

Peking University

72.75881.001

50

Mar 03, 2018		AVIQA (single model)

aviqa team

72.48580.550

51

Nov 06, 2017		attention+self-attention (single model)

guotong1988

71.69880.462

52

Nov 01, 2016		Dynamic Coattention Networks (ensemble)

Salesforce Research

https://arxiv.org/abs/1611.01604		71.62580.383

53

Jul 14, 2017		smarnet (single model)

Eigen Technology & Zhejiang University

https://arxiv.org/abs/1710.02772		71.41580.160

54

Jul 14, 2017		Mnemonic Reader (single model)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798		70.99580.146

54

Apr 13, 2017		QFASE

NUS

71.89879.989

55

Apr 22, 2018		MAMCN (single model)

Samsung Research

70.98579.939

55

Oct 27, 2017		M-NET (single)

UFL

71.01679.835

55

May 23, 2018		AttReader (single)

College of Computer & Information Science, SouthWest University, Chongqing, China

71.37379.725

56

Dec 28, 2016		FastQAExt

German Research Center for Artificial Intelligence

https://arxiv.org/abs/1703.04816		70.84978.857

56

Apr 02, 2017		Ruminating Reader (single model)

New York University

https://arxiv.org/abs/1704.07415		70.63979.456

57

Mar 08, 2017		ReasoNet (single model)

MSR Redmond

https://arxiv.org/abs/1609.05284		70.55579.364

57

Mar 14, 2017		Document Reader (single model)

Facebook AI Research

https://arxiv.org/abs/1704.00051		70.73379.353

57

Mar 24, 2017		jNet (single model)

USTC & National Research Council Canada & York University

https://arxiv.org/abs/1703.04617		70.60779.821

58

Apr 14, 2017		Multi-Perspective Matching (single model)

IBM Research

https://arxiv.org/abs/1612.04211		70.38778.784

58

May 13, 2017		RaSoR (single model)

Google NY, Tel-Aviv University

https://arxiv.org/abs/1611.01436		70.84978.741

59

Aug 30, 2017		SimpleBaseline (single model)

Technical University of Vienna

69.60078.236

59

Feb 05, 2018		SSR-BiDAF

single model

69.44378.358

60

Apr 12, 2017		SEDT+BiDAF (single model)

CMU

https://arxiv.org/abs/1703.00572		68.47877.971

61

Jun 25, 2017		PQMN (single model)

KAIST & AIBrain & Crosscert

68.33177.783

62

Apr 12, 2017		T-gating (single model)

Peking University

68.13277.569

62

Jul 29, 2017		SEDT (single model)

CMU

https://arxiv.org/abs/1703.00572		68.16377.527

62

Jan 22, 2018		FABIR (Single Model)

in review

67.74477.605

62

Feb 22, 2018		Unnamed submission by null

68.47877.220

63

Dec 28, 2016		FastQA

German Research Center for Artificial Intelligence

https://arxiv.org/abs/1703.04816		68.43677.070

63

Feb 22, 2018		Unnamed submission by null

68.42577.077

63

Nov 28, 2016		BiDAF (single model)

Allen Institute for AI & University of Washington

https://arxiv.org/abs/1611.01603		67.97477.323

64

Sep 19, 2017		AllenNLP BiDAF (single model)

Allen Institute for AI

http://allennlp.org/		67.61877.151

64

Oct 26, 2016		Match-LSTM with Ans-Ptr (Boundary) (ensemble)

Singapore Management University

https://arxiv.org/abs/1608.07905		67.90177.022

65

Feb 05, 2017		Iterative Co-attention Network

Fudan University

67.50276.786

66

Nov 01, 2016		Dynamic Coattention Networks (single model)

Salesforce Research

https://arxiv.org/abs/1611.01604		66.23375.896

66

Jan 03, 2018		newtest

single model

66.52775.787

67

Feb 24, 2018		Unnamed submission by null

65.99275.469

68

Jan 03, 2018		baseline

single model

64.79674.272

69

Dec 09, 2017		Unnamed submission by ravioncodalab

64.43973.921

69

Oct 26, 2016		Match-LSTM with Bi-Ans-Ptr (Boundary)

Singapore Management University

https://arxiv.org/abs/1608.07905		64.74473.743

70

Feb 19, 2017		Attentive CNN context with LSTM

NLPR, CASIA

63.30673.463

71

Nov 02, 2016		Fine-Grained Gating

Carnegie Mellon University

https://arxiv.org/abs/1611.01724		62.44673.327

71

Sep 21, 2017		OTF dict+spelling (single)

University of Montreal

https://arxiv.org/abs/1706.00286		64.08373.056

72

Sep 21, 2017		OTF spelling (single)

University of Montreal

https://arxiv.org/abs/1706.00286		62.89772.016

73

Sep 21, 2017		OTF spelling+lemma (single)

University of Montreal

https://arxiv.org/abs/1706.00286		62.60471.968

74

Sep 28, 2016		Dynamic Chunk Reader

IBM

https://arxiv.org/abs/1610.09996		62.49970.956

75

Aug 27, 2016		Match-LSTM with Ans-Ptr (Boundary)

Singapore Management University

https://arxiv.org/abs/1608.07905		60.47470.695

76

Jan 05, 2018		PivRet (single model)

anonymous

58.76469.276

77

Aug 27, 2016		Match-LSTM with Ans-Ptr (Sentence)

Singapore Management University

https://arxiv.org/abs/1608.07905		54.50567.748