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

Google AI Language

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

2

Dec 21, 2018		PAML+BERT (ensemble model)

PINGAN GammaLab

83.45786.122

2

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

Layer 6 AI NLP Team

83.46986.043

2

Dec 13, 2018		BERT finetune baseline (ensemble)

Anonymous

83.53686.096

3

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

Layer 6 AI NLP Team

82.99586.035

4

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

Google AI Language

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

5

Dec 16, 2018		PAML+BERT (single model)

PINGAN GammaLab

82.57785.603

6

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

Joint Laboratory of HIT and iFLYTEK Research

82.37485.310

7

Dec 12, 2018		BERT finetune baseline (single model)

Anonymous

82.12684.820

7

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

42Maru NLP Team

82.12684.624

8

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

Joint Laboratory of HIT and iFLYTEK Research

81.17884.251

9

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

42Maru NLP Team

80.65983.562

10

Jan 07, 2019		Bert-raw (single model)

None

80.51283.539

11

Jan 07, 2019		BERT + NeurQuRI (single model)

2SAH

80.34383.221

11

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

42Maru NLP Team

80.38882.908

11

Dec 03, 2018		PwP+BERT (single model)

AITRICS

80.11783.189

12

Nov 08, 2018		BERT (single model)

Google AI Language

80.00583.061

13

Dec 06, 2018		NEXYS_BASE (single model)

NEXYS, DGIST R7

79.77982.912

14

Nov 09, 2018		L6Net + BERT (single model)

Layer 6 AI

79.18182.259

15

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

None

78.30181.350

16

Dec 14, 2018		BERT+AC(single model)

Hithink RoyalFlush

78.05281.174

17

Nov 06, 2018		SLQA+BERT (single model)

Alibaba DAMO NLP

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

18

Jan 05, 2019		synss (single model )

bert_finetune

76.05579.329

19

Dec 18, 2018		ARSG-BERT (single model)

TRINITI RESEARCH LABS, Active.ai

https://active.ai		74.74678.227

19

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

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

74.79177.988

20

Sep 13, 2018		nlnet (single model)

Microsoft Research Asia

74.27277.052

21

Dec 20, 2018		{Anonymous} (single model)

Anonymous

73.23476.790

21

Dec 29, 2018		MMIPN

Single

73.50576.424

22

Oct 12, 2018		YARCS (ensemble)

IBM Research AI

72.67075.507

23

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

Pingan Tech Olatop Lab

71.66675.457

23

Oct 13, 2018		RNANetSimple (ensemble)

Anonymous

72.58075.075

24

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

NUDT

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

24

Aug 28, 2018		SLQA+ (single model)

Alibaba DAMO NLP

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

24

Sep 17, 2018		Unet (ensemble)

Fudan University & Liulishuo Lab

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

25

Sep 14, 2018		SAN (ensemble model)

Microsoft Business Applications AI Research

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

26

Oct 13, 2018		RNANetSimple (single model)

Anonymous

70.71873.403

27

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

Chonbuk National University, Cognitive Computing Lab.

69.47672.857

28

Sep 14, 2018		Unet (single model)

Fudan University & Liulishuo Lab

69.26272.642

28

Aug 21, 2018		FusionNet++ (ensemble)

Microsoft Business Applications Group AI Research

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

29

Dec 20, 2018		DocQA + NeurQuRI (single model)

2SAH

68.76671.662

30

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

UW and FAIR

68.02171.583

30

Aug 21, 2018		SAN (single model)

Microsoft Business Applications AI Research

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

30

Aug 25, 2018		ARRR (single model)

anonymous

68.65371.124

31

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

Kangwon National University, Natural Language Processing Lab.

68.21370.878

31

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

Kangwon National University in South Korea

67.89770.884

32

Jan 01, 2019		EBB-Net (single model)

Enliple AI

66.61070.303

33

Jun 25, 2018		KakaoNet2 (single model)

Kakao NLP Team

65.71969.381

34

Jul 11, 2018		abcNet (single model)

Fudan University & Liulishuo AI Lab

65.25669.206

34

Sep 13, 2018		BiDAF++ (single model)

UW and FAIR

65.65168.866

35

Jun 27, 2018		BSAE AddText (single model)

reciTAL.ai

63.33867.422

36

Aug 14, 2018		eeAttNet (single model)

BBD NLP Team

https://www.bbdservice.com		63.32766.633

36

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

Allen Institute for Artificial Intelligence [modified by Stanford]

63.37266.251

37

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

Carnegie Mellon University

57.70762.341

37

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

Allen Institute for Artificial Intelligence [modified by Stanford]

59.33262.305

38

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

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

Dec 22, 2017		AttentionReader+ (ensemble)

Tencent DPDAC NLP

81.79088.163

10

Dec 23, 2018		MMIPN

Single

81.58088.948

10

Dec 17, 2017		r-net (ensemble)

Microsoft Research Asia

http://aka.ms/rnet		82.13688.126

10

Apr 23, 2018		r-net (single model)

Microsoft Research Asia

81.39188.170

10

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

Microsoft Research Asia & NUDT

81.53888.130

11

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

Microsoft Research Asia & NUDT

81.40188.122

12

Feb 27, 2018		QANet (single model)

Google Brain & CMU

80.92987.773

12

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

Kangwon National University, Natural Language Processing Lab.

81.49687.557

13

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

Allen Institute for Artificial Intelligence

81.00387.432

13

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

Microsoft Research Asia & NUDT

80.91987.492

14

Apr 12, 2018		AVIQA+ (ensemble)

aviqa team

80.61587.311

14

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

Microsoft Research Asia & NUDT

80.48987.454

15

Jan 13, 2018		SLQA+

single model

80.43687.021

16

Jan 12, 2018		EAZI+ (ensemble)

Yiwise NLP Group

80.42686.912

16

Jan 04, 2018		{EAZI} (ensemble)

Yiwise NLP Group

80.43686.912

17

Mar 20, 2018		DNET (ensemble)

QA geeks

80.16486.721

17

Jan 22, 2018		Hybrid AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

80.02787.288

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

Feb 23, 2018		MAMCN+ (single model)

Samsung Research

79.69286.727

19

Apr 10, 2018		Unnamed submission by null

80.02786.612

20

Dec 28, 2017		SLQA+ (single model)

Alibaba iDST NLP

79.19986.590

20

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

Microsoft Research Asia

79.90186.536

21

Dec 05, 2017		SAN (ensemble model)

Microsoft Business AI Solutions Team

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

22

Oct 17, 2017		Interactive AoA Reader+ (ensemble)

Joint Laboratory of HIT and iFLYTEK

79.08386.450

22

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

ForceWin, KP Lab.

79.08386.288

23

Jun 01, 2018		MDReader

single model

79.03186.006

23

Feb 01, 2018		Unnamed submission by null

78.99986.151

24

Oct 24, 2017		FusionNet (ensemble)

Microsoft Business AI Solutions Team

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

25

Oct 22, 2017		DCN+ (ensemble)

Salesforce Research

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

26

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

Allen Institute for Artificial Intelligence

78.58085.833

26

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

Kangwon National University, Natural Language Processing Lab.

78.66485.780

27

May 09, 2018		KakaoNet (single model)

Kakao NLP Team

78.40185.724

28

Nov 30, 2017		SLQA(ensemble)

Alibaba iDST NLP

78.32885.682

28

Jan 02, 2018		Conductor-net (ensemble)

CMU

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

28

Mar 19, 2018		aviqa (ensemble)

aviqa team

78.49685.469

28

Jun 01, 2018		MDReader0

single model

78.17185.543

28

Jan 03, 2018		MEMEN (single model)

Zhejiang University

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

28

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

UW and FAIR

78.22385.535

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

33

Sep 26, 2018		{gqa} (single model)

FAIR

77.09083.931

33

Dec 21, 2017		Jenga (ensemble)

Facebook AI Research

77.23784.466

33

Jan 23, 2018		MARS (single model)

YUANFUDAO research NLP

76.85984.739

34

Nov 01, 2017		SAN (single model)

Microsoft Business AI Solutions Team

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

35

Oct 13, 2017		r-net (single model)

Microsoft Research Asia

http://aka.ms/rnet		76.46184.265

35

Dec 19, 2017		FRC (single model)

in review

76.24084.599

35

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

Kangwon National University in South Korea

76.77584.491

36

Oct 22, 2017		Conductor-net (ensemble)

CMU

76.14683.991

37

Sep 08, 2017		FusionNet (single model)

Microsoft Business AI Solutions team

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

37

Oct 18, 2018		KAR (single model)

York University

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

38

Jul 14, 2017		smarnet (ensemble)

Eigen Technology & Zhejiang University

75.98983.475

38

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

Joint Laboratory of HIT and iFLYTEK

75.82183.843

38

Mar 15, 2018		AVIQA-v2 (single model)

aviqa team

75.92683.305

39

Oct 05, 2018		Unnamed submission by null

74.95083.294

39

Aug 18, 2017		RaSoR + TR (single model)

Tel-Aviv University

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

40

Oct 23, 2017		DCN+ (single model)

Salesforce Research

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

41

Feb 13, 2018		SSR-BiDAF

ensemble model

74.54182.477

41

Nov 01, 2017		Mixed model (ensemble)

Sean

75.26582.769

42

Jan 02, 2018		Conductor-net (single model)

CMU

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

42

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

guotong1988

75.22382.716

42

May 21, 2017		MEMEN (ensemble)

Eigen Technology & Zhejiang University

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

43

Mar 09, 2017		ReasoNet (ensemble)

MSR Redmond

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

44

Aug 14, 2018		eeAttNet (single model)

BBD NLP Team

https://www.bbdservice.com		74.60482.501

44

Jul 10, 2017		DCN+ (single model)

Salesforce Research

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

44

Feb 06, 2018		Jenga (single model)

Facebook AI Research

74.37382.845

44

Oct 27, 2017		Unnamed submission by null

74.48982.312

44

Oct 31, 2017		SLQA (single model)

Alibaba iDST NLP

74.48982.815

45

Jul 14, 2017		Mnemonic Reader (ensemble)

NUDT and Fudan University

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

46

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

Kangwon National University in South Korea

74.12182.342

47

Jul 29, 2017		SEDT (ensemble model)

CMU

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

48

Jul 06, 2017		SSAE (ensemble)

Tsinghua University

74.08081.665

48

Dec 14, 2017		Jenga (single model)

Facebook AI Research

73.30381.754

48

Nov 06, 2017		Conductor-net (single)

CMU

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

48

Jul 25, 2017		Interactive AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

73.63981.931

48

Apr 22, 2017		SEDT+BiDAF (ensemble)

CMU

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

48

Jan 24, 2017		Multi-Perspective Matching (ensemble)

IBM Research

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

48

Feb 22, 2017		BiDAF (ensemble)

Allen Institute for AI & University of Washington

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

49

May 01, 2017		jNet (ensemble)

USTC & National Research Council Canada & York University

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

50

Oct 22, 2017		Conductor-net (single)

CMU

72.59081.415

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

Apr 12, 2017		T-gating (ensemble)

Peking University

72.75881.001

50

Apr 17, 2018		Unnamed submission by null

72.83180.622

50

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

Allen Institute for Artificial Intelligence

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

51

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

Kangwon National University in South Korea

71.90881.023

51

Mar 03, 2018		AVIQA (single model)

aviqa team

72.48580.550

52

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

guotong1988

71.69880.462

53

Nov 01, 2016		Dynamic Coattention Networks (ensemble)

Salesforce Research

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

54

Jul 14, 2017		smarnet (single model)

Eigen Technology & Zhejiang University

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

55

Jul 14, 2017		Mnemonic Reader (single model)

NUDT and Fudan University

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

55

Apr 13, 2017		QFASE

NUS

71.89879.989

56

Apr 22, 2018		MAMCN (single model)

Samsung Research

70.98579.939

56

Oct 27, 2017		M-NET (single)

UFL

71.01679.835

57

Mar 24, 2017		jNet (single model)

USTC & National Research Council Canada & York University

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

57

May 23, 2018		AttReader (single)

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

71.37379.725

58

Apr 02, 2017		Ruminating Reader (single model)

New York University

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

58

May 13, 2017		RaSoR (single model)

Google NY, Tel-Aviv University

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

58

Mar 14, 2017		Document Reader (single model)

Facebook AI Research

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

58

Dec 28, 2016		FastQAExt

German Research Center for Artificial Intelligence

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

58

Mar 08, 2017		ReasoNet (single model)

MSR Redmond

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

59

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

IBM Research

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

60

Aug 30, 2017		SimpleBaseline (single model)

Technical University of Vienna

69.60078.236

60

Feb 05, 2018		SSR-BiDAF

single model

69.44378.358

61

Apr 12, 2017		SEDT+BiDAF (single model)

CMU

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

62

Jun 25, 2017		PQMN (single model)

KAIST & AIBrain & Crosscert

68.33177.783

63

Apr 12, 2017		T-gating (single model)

Peking University

68.13277.569

63

Jul 29, 2017		SEDT (single model)

CMU

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

64

Nov 28, 2016		BiDAF (single model)

Allen Institute for AI & University of Washington

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

64

Jan 22, 2018		FABIR

Single Model

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

64

Dec 28, 2016		FastQA

German Research Center for Artificial Intelligence

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

64

Feb 22, 2018		Unnamed submission by null

68.42577.077

64

Feb 22, 2018		Unnamed submission by null

68.47877.220

65

Sep 19, 2017		AllenNLP BiDAF (single model)

Allen Institute for AI

http://allennlp.org/		67.61877.151

65

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

Singapore Management University

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

66

Feb 05, 2017		Iterative Co-attention Network

Fudan University

67.50276.786

67

Nov 01, 2016		Dynamic Coattention Networks (single model)

Salesforce Research

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

67

Jan 03, 2018		newtest

single model

66.52775.787

68

Feb 24, 2018		Unnamed submission by null

65.99275.469

69

Jan 10, 2018		Unnamed submission by null

64.79674.272

70

Dec 09, 2017		Unnamed submission by ravioncodalab

64.43973.921

70

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

Singapore Management University

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

71

Feb 19, 2017		Attentive CNN context with LSTM

NLPR, CASIA

63.30673.463

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

72

Nov 02, 2016		Fine-Grained Gating

Carnegie Mellon University

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

72

Sep 21, 2017		OTF spelling+lemma (single)

University of Montreal

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

73

Sep 28, 2016		Dynamic Chunk Reader

IBM

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

74

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

Singapore Management University

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

75

Sep 11, 2018		Unnamed submission by Will_Wu

59.05869.436

76

Jan 10, 2018		Unnamed submission by null

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

78

Nov 14, 2018		Unnamed submission by jinhyuklee

52.54462.780

79

Oct 26, 2018		Unnamed submission by minjoon

52.53362.757