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.

Explore SQuAD2.0 and model predictionsSQuAD2.0 paper (Rajpurkar & Jia et al. '18)

SQuAD 1.1, the previous version of the SQuAD dataset, contains 100,000+ question-answer pairs on 500+ articles.

Explore SQuAD1.1 and model predictionsSQuAD1.0 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

Jan 15, 2019		BERT + MMFT + ADA (ensemble)

Microsoft Research Asia

85.08287.615

2

Jan 10, 2019		BERT + Synthetic Self-Training (ensemble)

Google AI Language

https://github.com/google-research/bert		84.29286.967

3

Dec 13, 2018		BERT finetune baseline (ensemble)

Anonymous

83.53686.096

4

Dec 16, 2018		Lunet + Verifier + BERT (ensemble)

Layer 6 AI NLP Team

83.46986.043

4

Dec 21, 2018		PAML+BERT (ensemble model)

PINGAN GammaLab

83.45786.122

5

Jan 10, 2019		BERT + Synthetic Self-Training (single model)

Google AI Language

https://github.com/google-research/bert		82.97285.810

5

Feb 16, 2019		Bert-raw (ensemble)

None

83.17585.635

5

Dec 15, 2018		Lunet + Verifier + BERT (single model)

Layer 6 AI NLP Team

82.99586.035

5

Jan 14, 2019		BERT + MMFT + ADA (single model)

Microsoft Research Asia

83.04085.892

6

Feb 15, 2019		BERT + NeurQuRI (ensemble)

2SAH

82.80385.703

7

Dec 16, 2018		PAML+BERT (single model)

PINGAN GammaLab

82.57785.603

8

Nov 16, 2018		AoA + DA + BERT (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

82.37485.310

8

Jan 13, 2019		Bert-raw (ensemble)

None

82.57784.884

9

Dec 12, 2018		BERT finetune baseline (single model)

Anonymous

82.12684.820

9

Dec 10, 2018		Candi-Net+BERT (ensemble)

42Maru NLP Team

82.12684.624

10

Feb 15, 2019		BERT + NeurQuRI (single model)

2SAH

81.25784.342

11

Nov 16, 2018		AoA + DA + BERT (single model)

Joint Laboratory of HIT and iFLYTEK Research

81.17884.251

12

Dec 19, 2018		Candi-Net+BERT (single model)

42Maru NLP Team

80.65983.562

13

Jan 22, 2019		BERT + NeurQuRI (single model)

2SAH

80.59183.391

13

Jan 07, 2019		Bert-raw (single model)

None

80.51283.539

14

Jan 07, 2019		BERT + NeurQuRI (single model)

2SAH

80.34383.221

14

Dec 05, 2018		Candi-Net+BERT (single model)

42Maru NLP Team

80.38882.908

14

Nov 08, 2018		BERT (single model)

Google AI Language

80.00583.061

15

Feb 12, 2019		BERT + Sparse-Transformer

single model

79.94883.023

16

Dec 06, 2018		NEXYS_BASE (single model)

NEXYS, DGIST R7

79.77982.912

16

Feb 16, 2019		Bert-raw (single model)

None

80.34383.243

17

Dec 03, 2018		PwP+BERT (single model)

AITRICS

80.11783.189

18

Feb 02, 2019		SJRC (single model)

Shanghai Jiao Tong University

79.71182.842

18

Feb 01, 2019		{bert-finetuning} (single model)

ksai

79.63282.852

19

Nov 09, 2018		L6Net + BERT (single model)

Layer 6 AI

79.18182.259

20

Jan 08, 2019		Bert-raw-full (single model)

None

78.30181.350

21

Dec 14, 2018		BERT+AC(single model)

Hithink RoyalFlush

78.05281.174

22

Nov 06, 2018		SLQA+BERT (single model)

Alibaba DAMO NLP

http://www.aclweb.org/anthology/P18-1158		77.00380.209

23

Jan 05, 2019		synss (single model )

bert_finetune

76.05579.329

24

Dec 18, 2018		ARSG-BERT (single model)

TRINITI RESEARCH LABS, Active.ai

https://active.ai		74.74678.227

24

Nov 05, 2018		MIR-MRC(F-Net) (single model)

Kangwon National University, Natural Language Processing Lab. & ForceWin, KP Lab.

74.79177.988

25

Sep 13, 2018		nlnet (single model)

Microsoft Research Asia

74.27277.052

26

Dec 20, 2018		{Anonymous} (single model)

Anonymous

73.23476.790

26

Dec 29, 2018		MMIPN

Single

73.50576.424

27

Oct 12, 2018		YARCS (ensemble)

IBM Research AI

72.67075.507

28

Nov 14, 2018		BERT+Answer Verifier (single model)

Pingan Tech Olatop Lab

71.66675.457

28

Oct 13, 2018		RNANetSimple (ensemble)

Anonymous

72.58075.075

29

Sep 17, 2018		Unet (ensemble)

Fudan University & Liulishuo Lab

https://arxiv.org/abs/1810.06638		71.41774.869

29

Aug 28, 2018		SLQA+ (single model)

Alibaba DAMO NLP

http://www.aclweb.org/anthology/P18-1158		71.46274.434

29

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

NUDT

https://arxiv.org/abs/1808.05759		71.76774.295

30

Sep 14, 2018		SAN (ensemble model)

Microsoft Business Applications AI Research

https://arxiv.org/abs/1712.03556		71.31673.704

30

Jan 19, 2019		{BERT-base} (single-model)

Anonymous

70.76374.449

31

Oct 13, 2018		RNANetSimple (single model)

Anonymous

70.71873.403

32

Sep 26, 2018		Multi-Level Attention Fusion(MLAF) (single model)

Chonbuk National University, Cognitive Computing Lab.

69.47672.857

33

Sep 14, 2018		Unet (single model)

Fudan University & Liulishuo Lab

69.26272.642

33

Aug 21, 2018		FusionNet++ (ensemble)

Microsoft Business Applications Group AI Research

https://arxiv.org/abs/1711.07341		70.30072.484

34

Dec 20, 2018		DocQA + NeurQuRI (single model)

2SAH

68.76671.662

35

Aug 21, 2018		SAN (single model)

Microsoft Business Applications AI Research

https://arxiv.org/abs/1712.03556		68.65371.439

35

Aug 25, 2018		ARRR (single model)

anonymous

68.65371.124

36

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

Kangwon National University in South Korea

67.89770.884

36

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

Kangwon National University, Natural Language Processing Lab.

68.21370.878

36

Sep 13, 2018		BiDAF++ with pair2vec (single model)

UW and FAIR

68.02171.583

37

Jan 01, 2019		EBB-Net (single model)

Enliple AI

66.61070.303

38

Jun 25, 2018		KakaoNet2 (single model)

Kakao NLP Team

65.71969.381

39

Jul 11, 2018		abcNet (single model)

Fudan University & Liulishuo AI Lab

65.25669.206

39

Sep 13, 2018		BiDAF++ (single model)

UW and FAIR

65.65168.866

40

Jun 27, 2018		BSAE AddText (single model)

reciTAL.ai

63.33867.422

41

Aug 14, 2018		eeAttNet (single model)

BBD NLP Team

https://www.bbdservice.com		63.32766.633

41

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

Allen Institute for Artificial Intelligence [modified by Stanford]

63.37266.251

42

Nov 27, 2018		Tree-LSTM + BiDAF + ELMo (single model)

Carnegie Mellon University

57.70762.341

42

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

Allen Institute for Artificial Intelligence [modified by Stanford]

59.33262.305

43

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

University of Washington [modified by Stanford]

59.17462.093

SQuAD1.1 Leaderboard

Here are the ExactMatch (EM) and F1 scores evaluated on the test set of SQuAD v1.1.

RankModelEMF1
Human Performance

Stanford University

(Rajpurkar et al. '16)		82.30491.221

1

Oct 05, 2018		BERT (ensemble)

Google AI Language

https://arxiv.org/abs/1810.04805		87.43393.160

2

Oct 05, 2018		BERT (single model)

Google AI Language

https://arxiv.org/abs/1810.04805		85.08391.835

2

Sep 09, 2018		nlnet (ensemble)

Microsoft Research Asia

85.35691.202

2

Sep 26, 2018		nlnet (ensemble)

Microsoft Research Asia

85.95491.677

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

Mar 19, 2018		QANet (ensemble)

Google Brain & CMU

83.87789.737

5

Sep 09, 2018		nlnet (single model)

Microsoft Research Asia

83.46890.133

5

Jun 20, 2018		MARS (ensemble)

YUANFUDAO research NLP

83.98289.796

6

Sep 01, 2018		MARS (single model)

YUANFUDAO research NLP

83.18589.547

7

Jan 22, 2018		Hybrid AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

82.48289.281

7

Jun 21, 2018		MARS (single model)

YUANFUDAO research NLP

83.12289.224

7

Jun 20, 2018		QANet (single)

Google Brain & CMU

82.47189.306

7

Mar 06, 2018		QANet (ensemble)

Google Brain & CMU

82.74489.045

7

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

Microsoft Research Asia & NUDT

82.84988.764

8

Feb 27, 2018		QANet (single model)

Google Brain & CMU

82.20988.608

8

Feb 02, 2018		Reinforced Mnemonic Reader (ensemble model)

NUDT and Fudan University

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

8

Jan 03, 2018		r-net+ (ensemble)

Microsoft Research Asia

82.65088.493

8

May 09, 2018		MARS (single model)

YUANFUDAO research NLP

82.58788.880

9

Jan 05, 2018		SLQA+ (ensemble)

Alibaba iDST NLP

82.44088.607

10

Apr 23, 2018		r-net (single model)

Microsoft Research Asia

81.39188.170

10

Dec 22, 2017		AttentionReader+ (ensemble)

Tencent DPDAC NLP

81.79088.163

10

Dec 23, 2018		MMIPN

Single

81.58088.948

11

Dec 17, 2018		ARSG-BERT (single model)

TRINITI RESEARCH LABS, Active.ai

https://active.ai		81.30788.909

11

Dec 17, 2017		r-net (ensemble)

Microsoft Research Asia

http://aka.ms/rnet		82.13688.126

11

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

Microsoft Research Asia & NUDT

81.53888.130

12

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

Kangwon National University, Natural Language Processing Lab.

81.49687.557

12

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

Microsoft Research Asia & NUDT

81.40188.122

13

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

Microsoft Research Asia & NUDT

80.48987.454

13

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

Allen Institute for Artificial Intelligence

81.00387.432

13

Feb 27, 2018		QANet (single model)

Google Brain & CMU

80.92987.773

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

16

Jan 13, 2018		SLQA+

single model

80.43687.021

17

Jan 12, 2018		EAZI+ (ensemble)

Yiwise NLP Group

80.42686.912

17

Jan 04, 2018		{EAZI} (ensemble)

Yiwise NLP Group

80.43686.912

18

Mar 20, 2018		DNET (ensemble)

QA geeks

80.16486.721

18

Jan 22, 2018		Hybrid AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

80.02787.288

19

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

Microsoft Research Asia & NUDT

79.99686.711

20

Jan 29, 2018		Reinforced Mnemonic Reader (single model)

NUDT and Fudan University

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

20

Feb 23, 2018		MAMCN+ (single model)

Samsung Research

79.69286.727

20

Apr 10, 2018		Unnamed submission by null

80.02786.612

21

Dec 28, 2017		SLQA+ (single model)

Alibaba iDST NLP

79.19986.590

21

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

Microsoft Research Asia

79.90186.536

22

Dec 05, 2017		SAN (ensemble model)

Microsoft Business AI Solutions Team

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

23

Oct 17, 2017		Interactive AoA Reader+ (ensemble)

Joint Laboratory of HIT and iFLYTEK

79.08386.450

23

Nov 05, 2018		MIR-MRC(F-Net) (single model)

ForceWin, KP Lab.

79.08386.288

24

Jun 01, 2018		MDReader

single model

79.03186.006

24

Feb 01, 2018		Unnamed submission by null

78.99986.151

25

Oct 24, 2017		FusionNet (ensemble)

Microsoft Business AI Solutions Team

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

26

Oct 22, 2017		DCN+ (ensemble)

Salesforce Research

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

27

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

Allen Institute for Artificial Intelligence

78.58085.833

27

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

Kangwon National University, Natural Language Processing Lab.

78.66485.780

28

May 09, 2018		KakaoNet (single model)

Kakao NLP Team

78.40185.724

29

Nov 30, 2017		SLQA(ensemble)

Alibaba iDST NLP

78.32885.682

29

Jan 02, 2018		Conductor-net (ensemble)

CMU

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

29

Mar 19, 2018		aviqa (ensemble)

aviqa team

78.49685.469

29

Jun 01, 2018		MDReader0

single model

78.17185.543

29

Jan 03, 2018		MEMEN (single model)

Zhejiang University

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

29

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

UW and FAIR

78.22385.535

30

Jan 29, 2018		test

single

78.08785.348

31

Jul 25, 2017		Interactive AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

77.84585.297

32

Jan 10, 2018		Unnamed submission by null

77.43685.130

32

Mar 20, 2018		DNET (single model)

QA geeks

77.64684.905

33

Sep 18, 2018		BiDAF++ (single model)

UW and FAIR

77.57384.858

33

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

Tel-Aviv University

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

33

Apr 10, 2018		Unnamed submission by null

77.48984.735

33

Dec 06, 2017		AttentionReader+ (single)

Tencent DPDAC NLP

77.34284.925

34

Nov 06, 2017		Conductor-net (ensemble)

CMU

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

34

Sep 26, 2018		{gqa} (single model)

FAIR

77.09083.931

34

Dec 21, 2017		Jenga (ensemble)

Facebook AI Research

77.23784.466

34

Jan 23, 2018		MARS (single model)

YUANFUDAO research NLP

76.85984.739

35

Nov 01, 2017		SAN (single model)

Microsoft Business AI Solutions Team

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

36

Oct 13, 2017		r-net (single model)

Microsoft Research Asia

http://aka.ms/rnet		76.46184.265

36

Dec 19, 2017		FRC (single model)

in review

76.24084.599

36

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

Kangwon National University in South Korea

76.77584.491

37

Oct 22, 2017		Conductor-net (ensemble)

CMU

76.14683.991

38

Sep 08, 2017		FusionNet (single model)

Microsoft Business AI Solutions team

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

38

Oct 18, 2018		KAR (single model)

York University

https://arxiv.org/abs/1809.03449		76.12583.538

39

Jul 14, 2017		smarnet (ensemble)

Eigen Technology & Zhejiang University

75.98983.475

39

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

Joint Laboratory of HIT and iFLYTEK

75.82183.843

39

Mar 15, 2018		AVIQA-v2 (single model)

aviqa team

75.92683.305

40

Oct 05, 2018		Unnamed submission by null

74.95083.294

40

Aug 18, 2017		RaSoR + TR (single model)

Tel-Aviv University

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

41

Oct 23, 2017		DCN+ (single model)

Salesforce Research

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

42

Feb 13, 2018		SSR-BiDAF

ensemble model

74.54182.477

42

Nov 01, 2017		Mixed model (ensemble)

Sean

75.26582.769

43

Jan 02, 2018		Conductor-net (single model)

CMU

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

43

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

guotong1988

75.22382.716

43

May 21, 2017		MEMEN (ensemble)

Eigen Technology & Zhejiang University

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

44

Mar 09, 2017		ReasoNet (ensemble)

MSR Redmond

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

45

Aug 14, 2018		eeAttNet (single model)

BBD NLP Team

https://www.bbdservice.com		74.60482.501

45

Jul 10, 2017		DCN+ (single model)

Salesforce Research

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

45

Feb 06, 2018		Jenga (single model)

Facebook AI Research

74.37382.845

45

Oct 27, 2017		Unnamed submission by null

74.48982.312

45

Oct 31, 2017		SLQA (single model)

Alibaba iDST NLP

74.48982.815

46

Jul 14, 2017		Mnemonic Reader (ensemble)

NUDT and Fudan University

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

47

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

Kangwon National University in South Korea

74.12182.342

48

Jul 29, 2017		SEDT (ensemble model)

CMU

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

49

Jul 06, 2017		SSAE (ensemble)

Tsinghua University

74.08081.665

49

Dec 14, 2017		Jenga (single model)

Facebook AI Research

73.30381.754

49

Nov 06, 2017		Conductor-net (single)

CMU

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

49

Jul 25, 2017		Interactive AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

73.63981.931

49

Apr 22, 2017		SEDT+BiDAF (ensemble)

CMU

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

49

Jan 24, 2017		Multi-Perspective Matching (ensemble)

IBM Research

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

49

Feb 22, 2017		BiDAF (ensemble)

Allen Institute for AI & University of Washington

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

50

May 01, 2017		jNet (ensemble)

USTC & National Research Council Canada & York University

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

51

Oct 22, 2017		Conductor-net (single)

CMU

72.59081.415

51

Apr 17, 2018		Unnamed submission by null

72.83180.622

51

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

guotong1988

72.60081.011

51

Apr 12, 2017		T-gating (ensemble)

Peking University

72.75881.001

51

Apr 17, 2018		Unnamed submission by null

72.83180.622

51

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

Allen Institute for Artificial Intelligence

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

52

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

Kangwon National University in South Korea

71.90881.023

52

Mar 03, 2018		AVIQA (single model)

aviqa team

72.48580.550

53

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

guotong1988

71.69880.462

54

Nov 01, 2016		Dynamic Coattention Networks (ensemble)

Salesforce Research

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

55

Jul 14, 2017		smarnet (single model)

Eigen Technology & Zhejiang University

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

56

Jul 14, 2017		Mnemonic Reader (single model)

NUDT and Fudan University

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

56

Apr 13, 2017		QFASE

NUS

71.89879.989

57

Apr 22, 2018		MAMCN (single model)

Samsung Research

70.98579.939

57

Oct 27, 2017		M-NET (single)

UFL

71.01679.835

58

Mar 24, 2017		jNet (single model)

USTC & National Research Council Canada & York University

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

58

May 23, 2018		AttReader (single)

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

71.37379.725

59

Apr 02, 2017		Ruminating Reader (single model)

New York University

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

59

May 13, 2017		RaSoR (single model)

Google NY, Tel-Aviv University

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

59

Mar 14, 2017		Document Reader (single model)

Facebook AI Research

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

59

Dec 28, 2016		FastQAExt

German Research Center for Artificial Intelligence

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

59

Mar 08, 2017		ReasoNet (single model)

MSR Redmond

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

60

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

IBM Research

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

61

Aug 30, 2017		SimpleBaseline (single model)

Technical University of Vienna

69.60078.236

61

Feb 05, 2018		SSR-BiDAF

single model

69.44378.358

62

Apr 12, 2017		SEDT+BiDAF (single model)

CMU

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

63

Jun 25, 2017		PQMN (single model)

KAIST & AIBrain & Crosscert

68.33177.783

64

Apr 12, 2017		T-gating (single model)

Peking University

68.13277.569

64

Jul 29, 2017		SEDT (single model)

CMU

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

65

Nov 28, 2016		BiDAF (single model)

Allen Institute for AI & University of Washington

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

65

Jan 22, 2018		FABIR

Single Model

https://arxiv.org/abs/1810.09580		67.74477.605

65

Dec 28, 2016		FastQA

German Research Center for Artificial Intelligence

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

65

Feb 22, 2018		Unnamed submission by null

68.42577.077

65

Feb 22, 2018		Unnamed submission by null

68.47877.220

66

Sep 19, 2017		AllenNLP BiDAF (single model)

Allen Institute for AI

http://allennlp.org/		67.61877.151

66

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

Singapore Management University

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

67

Feb 05, 2017		Iterative Co-attention Network

Fudan University

67.50276.786

68

Nov 01, 2016		Dynamic Coattention Networks (single model)

Salesforce Research

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

68

Jan 03, 2018		newtest

single model

66.52775.787

69

Feb 24, 2018		Unnamed submission by null

65.99275.469

70

Jan 10, 2018		Unnamed submission by null

64.79674.272

71

Dec 09, 2017		Unnamed submission by ravioncodalab

64.43973.921

71

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

Singapore Management University

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

72

Feb 19, 2017		Attentive CNN context with LSTM

NLPR, CASIA

63.30673.463

72

Sep 21, 2017		OTF dict+spelling (single)

University of Montreal

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

73

Sep 21, 2017		OTF spelling (single)

University of Montreal

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

73

Nov 02, 2016		Fine-Grained Gating

Carnegie Mellon University

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

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

Sep 11, 2018		Unnamed submission by Will_Wu

59.05869.436

77

Jan 10, 2018		Unnamed submission by null

58.76469.276

78

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

Singapore Management University

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

79

Nov 14, 2018		Unnamed submission by jinhyuklee

52.54462.780

80

Oct 26, 2018		Unnamed submission by minjoon

52.53362.757