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

Mar 20, 2019		BERT + DAE + AoA (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

87.14789.474

2

Mar 15, 2019		BERT + ConvLSTM + MTL + Verifier (ensemble)

Layer 6 AI

86.73089.286

3

Mar 05, 2019		BERT + N-Gram Masking + Synthetic Self-Training (ensemble)

Google AI Language

https://github.com/google-research/bert		86.67389.147

4

Mar 16, 2019		BERT + DAE + AoA (single model)

Joint Laboratory of HIT and iFLYTEK Research

85.88488.621

5

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

Microsoft Research Asia

85.08287.615

5

Mar 13, 2019		BERT + ConvLSTM + MTL + Verifier (single model)

Layer 6 AI

84.92488.204

5

Mar 05, 2019		BERT + N-Gram Masking + Synthetic Self-Training (single model)

Google AI Language

https://github.com/google-research/bert		85.15087.715

6

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

Google AI Language

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

7

Dec 13, 2018		BERT finetune baseline (ensemble)

Anonymous

83.53686.096

7

Mar 20, 2019		Bert-raw (ensemble)

None

83.60486.036

7

Dec 21, 2018		PAML+BERT (ensemble model)

PINGAN GammaLab

83.45786.122

7

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

Layer 6 AI NLP Team

83.46986.043

8

Mar 04, 2019		SemBERT (ensemble model)

Shanghai Jiao Tong University

83.24385.821

8

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

Layer 6 AI NLP Team

82.99586.035

8

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

Microsoft Research Asia

83.04085.892

9

Feb 15, 2019		BERT + NeurQuRI (ensemble)

2SAH

82.80385.703

9

Feb 16, 2019		Bert-raw (ensemble)

None

83.17585.635

10

Dec 16, 2018		PAML+BERT (single model)

PINGAN GammaLab

82.57785.603

10

Feb 27, 2019		BERT + NeurQuRI (ensemble)

2SAH

82.71385.584

10

Mar 11, 2019		Bert-raw (ensemble)

None

83.11985.510

10

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

Google AI Language

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

10

Feb 26, 2019		BERT with Something (ensemble)

Anonymous

83.05185.737

11

Mar 02, 2019		SemBERT (single model)

Shanghai Jiao Tong University

82.43185.178

11

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

Joint Laboratory of HIT and iFLYTEK Research

82.37485.310

12

Dec 12, 2018		BERT finetune baseline (single model)

Anonymous

82.12684.820

12

Feb 28, 2019		BERT_s (single model)

Anonymous

81.97984.846

12

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

42Maru NLP Team

82.12684.624

13

Feb 28, 2019		BERT-large+UBFT (single model)

anonymous

81.57384.535

14

Feb 25, 2019		BERT with Something (single model)

Anonymous

81.11084.386

14

Feb 15, 2019		BERT + NeurQuRI (single model)

2SAH

81.25784.342

15

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

Joint Laboratory of HIT and iFLYTEK Research

81.17884.251

16

Mar 20, 2019		Bert-raw (single)

None

80.69383.922

16

Mar 07, 2019		BERT + UnAnsQ (single model)

Anonymous

80.74983.851

17

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

42Maru NLP Team

80.65983.562

18

Jan 09, 2019		Unnamed submission by null

80.51283.539

19

Mar 11, 2019		Bert-raw (single)

None

80.41183.457

19

Jan 22, 2019		BERT + NeurQuRI (single model)

2SAH

80.59183.391

20

Feb 16, 2019		Bert-raw (single model)

None

80.34383.243

20

Jan 09, 2019		Unnamed submission by null

80.34383.221

21

Feb 19, 2019		BERT + UDA (single model)

Anonymous

80.00583.208

21

Dec 03, 2018		PwP+BERT (single model)

AITRICS

80.11783.189

22

Mar 27, 2019		Insight-baseline (single model)

PAII Insight Team

79.77983.099

22

Nov 08, 2018		BERT (single model)

Google AI Language

80.00583.061

23

Feb 12, 2019		BERT + Sparse-Transformer

single model

79.94883.023

24

Mar 07, 2019		BERT uncased (single model)

Anonymous

79.74583.020

24

Feb 28, 2019		ST_bl

single model

80.14082.962

25

Dec 06, 2018		NEXYS_BASE (single model)

NEXYS, DGIST R7

79.77982.912

25

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

42Maru NLP Team

80.38882.908

26

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

ksai

79.63282.852

27

Mar 14, 2019		{Anonymous} (single model)

Anonymous

78.87682.524

27

Nov 09, 2018		L6Net + BERT (single model)

Layer 6 AI

79.18182.259

28

Mar 14, 2019		BISAN (single model)

Seoul National University & Hyundai Motors

78.48181.531

29

Jan 09, 2019		Unnamed submission by null

78.30181.350

30

Dec 14, 2018		BERT+AC(single model)

Hithink RoyalFlush

78.05281.174

31

Nov 06, 2018		SLQA+BERT (single model)

Alibaba DAMO NLP

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

32

Jan 05, 2019		synss (single model )

bert_finetune

76.05579.329

33

Dec 18, 2018		ARSG-BERT (single model)

TRINITI RESEARCH LABS, Active.ai

https://active.ai		74.74678.227

33

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

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

74.79177.988

34

Sep 13, 2018		nlnet (single model)

Microsoft Research Asia

74.27277.052

35

Dec 22, 2018		Unnamed submission by null

73.23476.790

35

Dec 29, 2018		MMIPN

Single

73.50576.424

36

Oct 12, 2018		YARCS (ensemble)

IBM Research AI

72.67075.507

37

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

Pingan Tech Olatop Lab

71.66675.457

37

Nov 10, 2018		Unnamed submission by null

72.58075.075

38

Sep 17, 2018		Unet (ensemble)

Fudan University & Liulishuo Lab

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

38

Aug 28, 2018		SLQA+ (single model)

Alibaba DAMO NLP

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

38

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

NUDT

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

38

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

Anonymous

70.76374.449

38

Sep 14, 2018		SAN (ensemble model)

Microsoft Business Applications AI Research

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

39

Nov 10, 2018		Unnamed submission by null

70.71873.403

40

Sep 14, 2018		Unet (single model)

Fudan University & Liulishuo Lab

69.26272.642

40

Aug 21, 2018		FusionNet++ (ensemble)

Microsoft Business Applications Group AI Research

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

40

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

Chonbuk National University, Cognitive Computing Lab.

69.47672.857

41

Dec 20, 2018		DocQA + NeurQuRI (single model)

2SAH

68.76671.662

42

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

UW and FAIR

68.02171.583

42

Aug 21, 2018		SAN (single model)

Microsoft Business Applications AI Research

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

42

Nov 10, 2018		Unnamed submission by null

68.65371.124

43

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

Kangwon National University in South Korea

67.89770.884

43

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

Kangwon National University, Natural Language Processing Lab.

68.21370.878

44

Jan 01, 2019		EBB-Net (single model)

Enliple AI

66.61070.303

45

Jun 25, 2018		KakaoNet2 (single model)

Kakao NLP Team

65.71969.381

46

Jul 11, 2018		abcNet (single model)

Fudan University & Liulishuo AI Lab

65.25669.206

46

Sep 13, 2018		BiDAF++ (single model)

UW and FAIR

65.65168.866

47

Jun 27, 2018		BSAE AddText (single model)

reciTAL.ai

63.33867.422

48

Aug 14, 2018		eeAttNet (single model)

BBD NLP Team

https://www.bbdservice.com		63.32766.633

48

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

Allen Institute for Artificial Intelligence [modified by Stanford]

63.37266.251

49

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

Carnegie Mellon University

57.70762.341

49

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

Allen Institute for Artificial Intelligence [modified by Stanford]

59.33262.305

50

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

Feb 14, 2019		Knowledge-enhanced BERT (single model)

Anonymous

85.94492.425

2

Sep 26, 2018		nlnet (ensemble)

Microsoft Research Asia

85.95491.677

3

Sep 09, 2018		nlnet (ensemble)

Microsoft Research Asia

85.35691.202

3

Oct 05, 2018		BERT (single model)

Google AI Language

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

4

Feb 19, 2019		WD (single model)

Anonymous

84.40290.561

4

Jul 11, 2018		QANet (ensemble)

Google Brain & CMU

84.45490.490

5

Feb 21, 2019		WD1 (single model)

Anonymous

83.80490.429

5

Jul 08, 2018		r-net (ensemble)

Microsoft Research Asia

84.00390.147

6

Jun 20, 2018		MARS (ensemble)

YUANFUDAO research NLP

83.98289.796

6

Sep 09, 2018		nlnet (single model)

Microsoft Research Asia

83.46890.133

6

Mar 19, 2018		QANet (ensemble)

Google Brain & CMU

83.87789.737

7

Sep 01, 2018		MARS (single model)

YUANFUDAO research NLP

83.18589.547

8

Jun 21, 2018		MARS (single model)

YUANFUDAO research NLP

83.12289.224

8

Jan 22, 2018		Hybrid AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

82.48289.281

8

May 09, 2018		MARS (single model)

YUANFUDAO research NLP

82.58788.880

8

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

Microsoft Research Asia & NUDT

82.84988.764

8

Jun 20, 2018		QANet (single)

Google Brain & CMU

82.47189.306

8

Mar 06, 2018		QANet (ensemble)

Google Brain & CMU

82.74489.045

9

Jan 05, 2018		SLQA+ (ensemble)

Alibaba iDST NLP

82.44088.607

9

Dec 17, 2018		ARSG-BERT (single model)

TRINITI RESEARCH LABS, Active.ai

https://active.ai		81.30788.909

9

Feb 02, 2018		Reinforced Mnemonic Reader (ensemble model)

NUDT and Fudan University

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

9

Jan 03, 2018		r-net+ (ensemble)

Microsoft Research Asia

82.65088.493

9

Dec 23, 2018		MMIPN

Single

81.58088.948

9

Feb 27, 2018		QANet (single model)

Google Brain & CMU

82.20988.608

10

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

Microsoft Research Asia & NUDT

81.53888.130

10

Apr 23, 2018		r-net (single model)

Microsoft Research Asia

81.39188.170

10

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

Microsoft Research Asia & NUDT

81.40188.122

10

Dec 22, 2017		AttentionReader+ (ensemble)

Tencent DPDAC NLP

81.79088.163

11

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

Kangwon National University, Natural Language Processing Lab.

81.49687.557

11

Dec 17, 2017		r-net (ensemble)

Microsoft Research Asia

http://aka.ms/rnet		82.13688.126

12

Feb 27, 2018		QANet (single model)

Google Brain & CMU

80.92987.773

13

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

Microsoft Research Asia & NUDT

80.91987.492

14

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

Microsoft Research Asia & NUDT

80.48987.454

14

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

Allen Institute for Artificial Intelligence

81.00387.432

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

Feb 23, 2018		MAMCN+ (single model)

Samsung Research

79.69286.727

18

Jan 22, 2018		Hybrid AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

80.02787.288

18

Mar 20, 2018		DNET (ensemble)

QA geeks

80.16486.721

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

Apr 10, 2018		Unnamed submission by null

80.02786.612

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

22

Dec 28, 2017		SLQA+ (single model)

Alibaba iDST NLP

79.19986.590

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

28

Mar 19, 2018		aviqa (ensemble)

aviqa team

78.49685.469

28

Jan 02, 2018		Conductor-net (ensemble)

CMU

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

28

Nov 30, 2017		SLQA(ensemble)

Alibaba iDST NLP

78.32885.682

29

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

UW and FAIR

78.22385.535

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

Jan 29, 2018		test

single

78.08785.348

30

Jul 25, 2017		Interactive AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

77.84585.297

31

Jan 10, 2018		Unnamed submission by null

77.43685.130

31

Mar 20, 2018		DNET (single model)

QA geeks

77.64684.905

32

Sep 18, 2018		BiDAF++ (single model)

UW and FAIR

77.57384.858

32

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

Tel-Aviv University

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

32

Apr 10, 2018		Unnamed submission by null

77.48984.735

32

Dec 06, 2017		AttentionReader+ (single)

Tencent DPDAC NLP

77.34284.925

33

Nov 06, 2017		Conductor-net (ensemble)

CMU

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

34

Dec 19, 2017		FRC (single model)

in review

76.24084.599

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

37

Oct 22, 2017		Conductor-net (ensemble)

CMU

76.14683.991

37

Sep 26, 2018		{gqa} (single model)

FAIR

77.09083.931

37

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

Kangwon National University in South Korea

76.77584.491

38

Sep 08, 2017		FusionNet (single model)

Microsoft Business AI Solutions team

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

39

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

Joint Laboratory of HIT and iFLYTEK

75.82183.843

39

Oct 18, 2018		KAR (single model)

York University

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

40

Jul 14, 2017		smarnet (ensemble)

Eigen Technology & Zhejiang University

75.98983.475

41

Mar 15, 2018		AVIQA-v2 (single model)

aviqa team

75.92683.305

42

Aug 18, 2017		RaSoR + TR (single model)

Tel-Aviv University

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

43

Oct 23, 2017		DCN+ (single model)

Salesforce Research

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

43

Oct 05, 2018		Unnamed submission by null

74.95083.294

43

May 21, 2017		MEMEN (ensemble)

Eigen Technology & Zhejiang University

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

43

Nov 01, 2017		Mixed model (ensemble)

Sean

75.26582.769

44

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

guotong1988

75.22382.716

44

Jul 10, 2017		DCN+ (single model)

Salesforce Research

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

45

Jan 02, 2018		Conductor-net (single model)

CMU

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

45

Feb 06, 2018		Jenga (single model)

Facebook AI Research

74.37382.845

45

Aug 14, 2018		eeAttNet (single model)

BBD NLP Team

https://www.bbdservice.com		74.60482.501

46

Feb 13, 2018		SSR-BiDAF

ensemble model

74.54182.477

46

Oct 31, 2017		SLQA (single model)

Alibaba iDST NLP

74.48982.815

46

Mar 09, 2017		ReasoNet (ensemble)

MSR Redmond

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

47

Jul 14, 2017		Mnemonic Reader (ensemble)

NUDT and Fudan University

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

48

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

Kangwon National University in South Korea

74.12182.342

48

Oct 27, 2017		Unnamed submission by null

74.48982.312

49

Jul 25, 2017		Interactive AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

73.63981.931

49

Jul 29, 2017		SEDT (ensemble model)

CMU

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

49

Nov 06, 2017		Conductor-net (single)

CMU

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

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

Feb 22, 2017		BiDAF (ensemble)

Allen Institute for AI & University of Washington

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

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

May 01, 2017		jNet (ensemble)

USTC & National Research Council Canada & York University

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

50

Apr 17, 2018		Unnamed submission by null

72.83180.622

50

Apr 17, 2018		Unnamed submission by null

72.83180.622

50

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

guotong1988

72.60081.011

50

Oct 22, 2017		Conductor-net (single)

CMU

72.59081.415

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

Apr 12, 2017		T-gating (ensemble)

Peking University

72.75881.001

53

Mar 03, 2018		AVIQA (single model)

aviqa team

72.48580.550

54

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

guotong1988

71.69880.462

55

Nov 01, 2016		Dynamic Coattention Networks (ensemble)

Salesforce Research

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

56

Jul 14, 2017		smarnet (single model)

Eigen Technology & Zhejiang University

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

56

Apr 13, 2017		QFASE

NUS

71.89879.989

57

Oct 27, 2017		M-NET (single)

UFL

71.01679.835

57

Apr 22, 2018		MAMCN (single model)

Samsung Research

70.98579.939

57

Jul 14, 2017		Mnemonic Reader (single model)

NUDT and Fudan University

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

57

May 23, 2018		AttReader (single)

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

71.37379.725

57

Mar 24, 2017		jNet (single model)

USTC & National Research Council Canada & York University

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

57

Apr 02, 2017		Ruminating Reader (single model)

New York University

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

57

Mar 14, 2017		Document Reader (single model)

Facebook AI Research

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

57

Dec 28, 2016		FastQAExt

German Research Center for Artificial Intelligence

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

57

May 13, 2017		RaSoR (single model)

Google NY, Tel-Aviv University

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

57

Mar 08, 2017		ReasoNet (single model)

MSR Redmond

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

58

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

IBM Research

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

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

63

Nov 28, 2016		BiDAF (single model)

Allen Institute for AI & University of Washington

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

63

Feb 22, 2018		Unnamed submission by null

68.47877.220

64

Feb 22, 2018		Unnamed submission by null

68.42577.077

64

Dec 28, 2016		FastQA

German Research Center for Artificial Intelligence

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

64

Jul 29, 2017		SEDT (single model)

CMU

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

65

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

Singapore Management University

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

65

Jan 22, 2018		FABIR

Single Model

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

66

Sep 19, 2017		AllenNLP BiDAF (single model)

Allen Institute for AI

http://allennlp.org/		67.61877.151

67

Feb 05, 2017		Iterative Co-attention Network

Fudan University

67.50276.786

68

Jan 03, 2018		newtest

single model

66.52775.787

68

Nov 01, 2016		Dynamic Coattention Networks (single model)

Salesforce Research

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

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

73

Nov 02, 2016		Fine-Grained Gating

Carnegie Mellon University

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

73

Sep 21, 2017		OTF dict+spelling (single)

University of Montreal

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

74

Sep 21, 2017		OTF spelling (single)

University of Montreal

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

75

Sep 21, 2017		OTF spelling+lemma (single)

University of Montreal

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

76

Sep 28, 2016		Dynamic Chunk Reader

IBM

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

77

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

Singapore Management University

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

78

Sep 11, 2018		Unnamed submission by Will_Wu

59.05869.436

79

Jan 10, 2018		Unnamed submission by null

58.76469.276

80

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

Singapore Management University

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

81

Nov 14, 2018		Unnamed submission by jinhyuklee

52.54462.780

82

Oct 26, 2018		Unnamed submission by minjoon

52.53362.757