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

1

Sep 13, 2018		nlnet (single model)

Microsoft Research Asia

74.23877.022

2

Sep 17, 2018		Unet (ensemble)

Fudan University & Liulishuo Lab

71.55375.011

2

Aug 15, 2018		Reinforced Mnemonic Reader + Answer Verifier (single model)

NUDT

https://arxiv.org/abs/1808.05759		71.69974.238

2

Aug 28, 2018		SLQA+ (single model)

Alibaba DAMO NLP

http://www.aclweb.org/anthology/P18-1158		71.45174.422

3

Sep 14, 2018		SAN (ensemble model)

Microsoft Business Applications Research Group

https://arxiv.org/abs/1712.03556		71.28273.658

4

Sep 14, 2018		Unet (single model)

Fudan University & Liulishuo Lab

69.23972.622

4

Aug 21, 2018		FusionNet++ (ensemble)

Microsoft Business Applications Group AI Research

https://arxiv.org/abs/1711.07341		70.34572.555

5

Sep 16, 2018		Candi-Net (single model)

42Maru NLP Team

69.90572.274

6

Sep 09, 2018		RNANetSimple (ensemble)

Anonymous

69.47672.060

7

Aug 21, 2018		SAN (single model)

Microsoft Business Applications Research Group

https://arxiv.org/abs/1712.03556		68.65371.441

8

Jul 13, 2018		VS^3-NET (single model)

Kangwon National University in South Korea

68.43871.282

8

Aug 25, 2018		ARRR (single model)

anonymous

68.64171.113

8

Sep 04, 2018		U-net (single model)

Fudan University & Liulishuo AI Lab

67.72771.676

8

Sep 13, 2018		Bidaf++

single model

68.02171.583

9

Aug 26, 2018		abcNet (single model)

Fudan University & Liulishuo AI Lab

66.67871.052

9

Jun 24, 2018		KACTEIL-MRC(GFN-Net) (single model)

Kangwon National University, Natural Language Processing Lab.

68.22470.871

10

Aug 30, 2018		Unnamed submission by null

67.73970.695

11

Sep 14, 2018		PAML (single model)

GammaLab

66.92670.534

12

Aug 25, 2018		FusionNet++ (single)

Microsoft Business Applications Group AI Research

https://arxiv.org/abs/1711.07341		66.55469.649

12

Sep 09, 2018		RNANetSimple (single model)

Anonymous

66.62269.385

13

Jun 25, 2018		KakaoNet2 (single model)

Kakao NLP Team

65.70869.369

14

Jul 11, 2018		abcNet (single model)

Fudan University & Liulishuo AI Lab

65.25669.198

14

Sep 13, 2018		BidafBase

single model

65.66268.870

15

Aug 23, 2018		Unnamed submission by null

64.87267.525

16

Jun 27, 2018		BSAE AddText (single model)

reciTAL.ai

63.38367.478

17

Aug 14, 2018		eeAttNet (single model)

BBD NLP Team

https://www.bbdservice.com		63.36066.638

17

May 30, 2018		BiDAF + Self Attention + ELMo (single model)

Allen Institute for Artificial Intelligence [modified by Stanford]

63.38366.262

18

Aug 11, 2018		Unnamed submission by null

62.50365.897

19

May 30, 2018		BiDAF + Self Attention (single model)

Allen Institute for Artificial Intelligence [modified by Stanford]

59.33262.305

20

May 30, 2018		BiDAF-No-Answer (single model)

University of Washington [modified by Stanford]

59.17462.093

SQuAD1.1 Leaderboard

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

RankModelEMF1
Human Performance

Stanford University

(Rajpurkar et al. '16)		82.30491.221

1

Sep 09, 2018		nlnet (ensemble)

Microsoft Research Asia

85.35691.202

2

Aug 28, 2018		nlnet (ensemble)

Microsoft Research Asia

85.10491.055

3

Jul 11, 2018		QANet (ensemble)

Google Brain & CMU

84.45490.490

4

Jul 08, 2018		r-net (ensemble)

Microsoft Research Asia

84.00390.147

5

Jun 20, 2018		MARS (ensemble)

YUANFUDAO research NLP

83.98289.796

6

Mar 19, 2018		QANet (ensemble)

Google Brain & CMU

83.87789.737

6

Sep 09, 2018		nlnet (single model)

Microsoft Research Asia

83.46890.133

7

Aug 28, 2018		nlnet (single model)

Microsoft Research Asia

83.45790.049

8

Sep 01, 2018		MARS (single model)

YUANFUDAO research NLP

83.18589.547

9

Jun 20, 2018		QANet (single)

Google Brain & CMU

82.47189.306

9

May 09, 2018		MARS (single model)

YUANFUDAO research NLP

82.58788.880

9

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

Microsoft Research Asia & NUDT

82.84988.764

9

Jan 22, 2018		Hybrid AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

82.48289.281

9

Jun 21, 2018		MARS (single model)

YUANFUDAO research NLP

83.12289.224

10

Mar 06, 2018		QANet (ensemble)

Google Brain & CMU

82.74489.045

11

Jan 03, 2018		r-net+ (ensemble)

Microsoft Research Asia

82.65088.493

11

Feb 02, 2018		Reinforced Mnemonic Reader (ensemble model)

NUDT and Fudan University

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

11

Feb 27, 2018		QANet (single model)

Google Brain & CMU

82.20988.608

11

Jan 05, 2018		SLQA+ (ensemble)

Alibaba iDST NLP

82.44088.607

12

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

Microsoft Research Asia & NUDT

81.53888.130

12

Dec 17, 2017		r-net (ensemble)

Microsoft Research Asia

http://aka.ms/rnet		82.13688.126

12

Apr 23, 2018		r-net (single model)

Microsoft Research Asia

81.39188.170

12

Dec 22, 2017		AttentionReader+ (ensemble)

Tencent DPDAC NLP

81.79088.163

13

Feb 27, 2018		QANet (single model)

Google Brain & CMU

80.92987.773

13

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

Kangwon National University, Natural Language Processing Lab.

81.49687.557

13

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

Microsoft Research Asia & NUDT

81.40188.122

14

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

Allen Institute for Artificial Intelligence

81.00387.432

14

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

Microsoft Research Asia & NUDT

80.91987.492

15

Apr 12, 2018		AVIQA+ (ensemble)

aviqa team

80.61587.311

15

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

Microsoft Research Asia & NUDT

80.48987.454

16

Jan 13, 2018		SLQA+

single model

80.43687.021

16

Jan 22, 2018		Hybrid AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

80.02787.288

16

Jan 04, 2018		{EAZI} (ensemble)

Yiwise NLP Group

80.43686.912

16

Jan 12, 2018		EAZI+ (ensemble)

Yiwise NLP Group

80.42686.912

17

Mar 20, 2018		DNET (ensemble)

QA geeks

80.16486.721

18

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

Microsoft Research Asia & NUDT

79.99686.711

19

Jan 29, 2018		Reinforced Mnemonic Reader (single model)

NUDT and Fudan University

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

19

Apr 10, 2018		Unnamed submission by null

80.02786.612

19

Feb 23, 2018		MAMCN+ (single model)

Samsung Research

79.69286.727

20

Dec 28, 2017		SLQA+ (single model)

Alibaba iDST NLP

79.19986.590

20

Dec 05, 2017		SAN (ensemble model)

Microsoft Business AI Solutions Team

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

20

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

Microsoft Research Asia

79.90186.536

21

Oct 17, 2017		Interactive AoA Reader+ (ensemble)

Joint Laboratory of HIT and iFLYTEK

79.08386.450

22

Oct 24, 2017		FusionNet (ensemble)

Microsoft Business AI Solutions Team

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

22

Jun 01, 2018		MDReader

single model

79.03186.006

22

Feb 01, 2018		Unnamed submission by null

78.99986.151

23

Oct 22, 2017		DCN+ (ensemble)

Salesforce Research

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

24

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

Kangwon National University, Natural Language Processing Lab.

78.66485.780

24

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

Allen Institute for Artificial Intelligence

78.58085.833

25

Jun 01, 2018		MDReader0

single model

78.17185.543

25

Sep 18, 2018		BiDAF++ with pair2vec (single model)

UW and FAIR

78.22385.535

25

Jan 02, 2018		Conductor-net (ensemble)

CMU

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

25

May 09, 2018		KakaoNet (single model)

Kakao NLP Team

78.40185.724

26

Nov 30, 2017		SLQA(ensemble)

Alibaba iDST NLP

78.32885.682

26

Mar 19, 2018		aviqa (ensemble)

aviqa team

78.49685.469

27

Jan 03, 2018		MEMEN (single model)

Zhejiang University

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

27

Jan 29, 2018		test

single

78.08785.348

28

Jul 25, 2017		Interactive AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

77.84585.297

29

Jan 10, 2018		Unnamed submission by null

77.43685.130

30

Dec 06, 2017		AttentionReader+ (single)

Tencent DPDAC NLP

77.34284.925

30

Sep 18, 2018		BiDAF++ (single model)

UW and FAIR

77.57384.858

30

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

Tel-Aviv University

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

30

Apr 10, 2018		Unnamed submission by null

77.48984.735

30

Mar 20, 2018		DNET (single model)

QA geeks

77.64684.905

31

Nov 06, 2017		Conductor-net (ensemble)

CMU

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

31

Dec 21, 2017		Jenga (ensemble)

Facebook AI Research

77.23784.466

31

Jan 23, 2018		MARS (single model)

YUANFUDAO research NLP

76.85984.739

32

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

Kangwon National University in South Korea

76.77584.491

32

Nov 01, 2017		SAN (single model)

Microsoft Business AI Solutions Team

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

33

Oct 13, 2017		r-net (single model)

Microsoft Research Asia

http://aka.ms/rnet		76.46184.265

33

Dec 19, 2017		FRC (single model)

in review

76.24084.599

34

Oct 22, 2017		Conductor-net (ensemble)

CMU

76.14683.991

35

Sep 08, 2017		FusionNet (single model)

Microsoft Business AI Solutions team

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

36

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

Joint Laboratory of HIT and iFLYTEK

75.82183.843

36

Jul 14, 2017		smarnet (ensemble)

Eigen Technology & Zhejiang University

75.98983.475

37

Mar 15, 2018		AVIQA-v2 (single model)

aviqa team

75.92683.305

38

Aug 18, 2017		RaSoR + TR (single model)

Tel-Aviv University

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

39

Oct 23, 2017		DCN+ (single model)

Salesforce Research

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

40

Feb 06, 2018		Jenga (single model)

Facebook AI Research

74.37382.845

40

Jul 10, 2017		DCN+ (single model)

Salesforce Research

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

40

Nov 01, 2017		Mixed model (ensemble)

Sean

75.26582.769

41

Jan 02, 2018		Conductor-net (single model)

CMU

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

41

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

guotong1988

75.22382.716

41

May 21, 2017		MEMEN (ensemble)

Eigen Technology & Zhejiang University

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

42

Mar 09, 2017		ReasoNet (ensemble)

MSR Redmond

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

43

Aug 14, 2018		eeAttNet (single model)

BBD NLP Team

https://www.bbdservice.com		74.60482.501

44

Feb 13, 2018		SSR-BiDAF

ensemble model

74.54182.477

44

Oct 31, 2017		SLQA (single model)

Alibaba iDST NLP

74.48982.815

44

Oct 27, 2017		Unnamed submission by null

74.48982.312

44

Jul 14, 2017		Mnemonic Reader (ensemble)

NUDT and Fudan University

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

45

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

Kangwon National University in South Korea

74.12182.342

46

Jul 29, 2017		SEDT (ensemble model)

CMU

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

47

Jul 06, 2017		SSAE (ensemble)

Tsinghua University

74.08081.665

47

Dec 14, 2017		Jenga (single model)

Facebook AI Research

73.30381.754

47

Jul 25, 2017		Interactive AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

73.63981.931

47

Apr 22, 2017		SEDT+BiDAF (ensemble)

CMU

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

47

Feb 22, 2017		BiDAF (ensemble)

Allen Institute for AI & University of Washington

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

47

Nov 06, 2017		Conductor-net (single)

CMU

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

47

Jan 24, 2017		Multi-Perspective Matching (ensemble)

IBM Research

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

47

May 01, 2017		jNet (ensemble)

USTC & National Research Council Canada & York University

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

48

Oct 22, 2017		Conductor-net (single)

CMU

72.59081.415

49

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

Kangwon National University in South Korea

71.90881.023

49

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

guotong1988

72.60081.011

49

Apr 12, 2017		T-gating (ensemble)

Peking University

72.75881.001

49

Apr 17, 2018		Unnamed submission by null

72.83180.622

49

Apr 17, 2018		Unnamed submission by null

72.83180.622

49

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

Allen Institute for Artificial Intelligence

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

49

Mar 03, 2018		AVIQA (single model)

aviqa team

72.48580.550

50

Nov 01, 2016		Dynamic Coattention Networks (ensemble)

Salesforce Research

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

51

Jul 14, 2017		smarnet (single model)

Eigen Technology & Zhejiang University

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

52

Jul 14, 2017		Mnemonic Reader (single model)

NUDT and Fudan University

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

52

Apr 13, 2017		QFASE

NUS

71.89879.989

52

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

guotong1988

71.69880.462

53

Apr 22, 2018		MAMCN (single model)

Samsung Research

70.98579.939

53

Oct 27, 2017		M-NET (single)

UFL

71.01679.835

54

Mar 24, 2017		jNet (single model)

USTC & National Research Council Canada & York University

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

54

May 23, 2018		AttReader (single)

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

71.37379.725

55

Apr 02, 2017		Ruminating Reader (single model)

New York University

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

55

May 13, 2017		RaSoR (single model)

Google NY, Tel-Aviv University

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

55

Mar 14, 2017		Document Reader (single model)

Facebook AI Research

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

55

Dec 28, 2016		FastQAExt

German Research Center for Artificial Intelligence

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

55

Mar 08, 2017		ReasoNet (single model)

MSR Redmond

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

56

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

IBM Research

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

57

Aug 30, 2017		SimpleBaseline (single model)

Technical University of Vienna

69.60078.236

57

Feb 05, 2018		SSR-BiDAF

single model

69.44378.358

58

Apr 12, 2017		SEDT+BiDAF (single model)

CMU

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

59

Jun 25, 2017		PQMN (single model)

KAIST & AIBrain & Crosscert

68.33177.783

60

Apr 12, 2017		T-gating (single model)

Peking University

68.13277.569

60

Jul 29, 2017		SEDT (single model)

CMU

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

61

Nov 28, 2016		BiDAF (single model)

Allen Institute for AI & University of Washington

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

61

Jan 22, 2018		FABIR (Single Model)

in review

67.74477.605

61

Dec 28, 2016		FastQA

German Research Center for Artificial Intelligence

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

61

Feb 22, 2018		Unnamed submission by null

68.42577.077

61

Feb 22, 2018		Unnamed submission by null

68.47877.220

62

Sep 19, 2017		AllenNLP BiDAF (single model)

Allen Institute for AI

http://allennlp.org/		67.61877.151

62

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

Singapore Management University

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

63

Feb 05, 2017		Iterative Co-attention Network

Fudan University

67.50276.786

64

Nov 01, 2016		Dynamic Coattention Networks (single model)

Salesforce Research

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

64

Jan 03, 2018		newtest

single model

66.52775.787

65

Feb 24, 2018		Unnamed submission by null

65.99275.469

66

Jan 10, 2018		Unnamed submission by null

64.79674.272

67

Dec 09, 2017		Unnamed submission by ravioncodalab

64.43973.921

67

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

Singapore Management University

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

68

Feb 19, 2017		Attentive CNN context with LSTM

NLPR, CASIA

63.30673.463

68

Sep 21, 2017		OTF dict+spelling (single)

University of Montreal

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

69

Sep 21, 2017		OTF spelling (single)

University of Montreal

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

69

Nov 02, 2016		Fine-Grained Gating

Carnegie Mellon University

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

69

Sep 21, 2017		OTF spelling+lemma (single)

University of Montreal

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

70

Sep 28, 2016		Dynamic Chunk Reader

IBM

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

71

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

Singapore Management University

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

72

Sep 11, 2018		Unnamed submission by Will_Wu

59.05869.436

73

Jan 05, 2018		PivRet (single model)

anonymous

58.76469.276

74

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

Singapore Management University

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