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