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