HotpotQA

A Dataset for Diverse, Explainable Multi-hop Question Answering

What is HotpotQA?

HotpotQA is a question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems. It is collected by a team of NLP researchers at Carnegie Mellon University, Stanford University, and Université de Montréal.

For more details about HotpotQA, please refer to our EMNLP 2018 paper:

Getting started

HotpotQA is distributed under a CC BY-SA 4.0 License. The training and development sets can be downloaded below.

A more comprehensive summary about data download, preprocessing, baseline model training, and evaluation is included in our GitHub repository, and linked below.

Once you have built your model, you can use the evaluation script we provide below to evaluate model performance by running python hotpot_evaluate_v1.py <path_to_prediction> <path_to_gold>

To submit your models and evaluate them on the official test sets, please read our submission guide hosted on Codalab.

We also release the processed Wikipedia used in the process of creating HotpotQA (also under a CC BY-SA 4.0 License), serving both as the corpus for the fullwiki setting in our evaluation, and hopefully as a standalone resource for future researches involving processed text on Wikipedia. Below please find the link to the documentation for this corpus.

Stay connected!

Join our Google group to receive updates or initiate discussions about HotpotQA!

If you use HotpotQA in your research, please cite our paper with the following BibTeX entry

@inproceedings{yang2018hotpotqa,
  title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
  author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.},
  booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})},
  year={2018}
}
Leaderboard (Distractor Setting)
In the distractor setting, a question-answering system reads 10 paragraphs to provide an answer (Ans) to a question. They must also justify these answers with supporting facts (Sup).
Model Code Ans Sup Joint
EM F1 EM F1 EM F1
1
Sep 27, 2019		 HGN (single model)
Microsoft Dynamics 365 AI Research
66.07 79.36 60.33 87.33 43.57 71.03
2
Jul 29, 2019		 TAP 2 (ensemble)
66.64 79.82 57.21 86.69 41.21 70.65
3
Oct 1, 2019		 EPS + BERT(wwm) (single model)
Anonymous
65.79 79.05 58.50 86.26 42.47 70.48
4
Jul 29, 2019		 TAP 2 (single model)
64.99 78.59 55.47 85.57 39.77 69.12
5
May 31, 2019		 EPS + BERT(large) (single model)
Anonymous
63.29 76.36 58.25 85.60 41.39 67.92
6
Aug 31, 2019		 SAE (single model)
Anonymous
60.36 73.58 56.93 84.63 38.81 64.96
7
Jun 13, 2019		 P-BERT (single model)
Anonymous
61.18 74.16 51.38 82.76 35.42 63.79
8
Sep 16, 2019		 LQR-net 2 + BERT-Base (single model)
Anonymous
60.20 73.78 56.21 84.09 36.56 63.68
9
Apr 11, 2019		 EPS + BERT (single model)
Anonymous
60.13 73.31 52.55 83.20 35.40 63.41
10
May 16, 2019		 PIPE (single model)
Anonymous
59.77 72.77 52.53 82.82 35.54 62.92
11
Jun 8, 2019		 TAP (single model)
58.63 71.48 46.84 82.98 32.03 61.90
12
Aug 14, 2019		 SAQA (single model)
Anonymous
55.07 70.22 57.62 84.19 35.94 61.72
13
Sep 2, 2019		 MKGN (single model)
Anonymous
57.09 70.69 54.26 83.54 35.59 61.69
14
Apr 19, 2019		 GRN + BERT (single model)
Anonymous
55.12 68.98 52.55 84.06 32.88 60.31
15
Jun 19, 2019		 LQR-net + BERT-Base (single model)
Anonymous
57.20 70.66 50.20 82.42 31.18 59.99
16
Apr 22, 2019		 DFGN (single model)
Shanghai Jiao Tong University & ByteDance AI Lab
(Xiao, Qu, Qiu et al. ACL19)		 56.31 69.69 51.50 81.62 33.62 59.82
17
Nov 21, 2018		 QFE (single model)
NTT Media Intelligence Laboratories
(Nishida et al., ACL'19)
53.86 68.06 57.75 84.49 34.63 59.61
18
Apr 17, 2019		 LQR-net (ensemble)
Anonymous
55.19 69.55 47.15 82.42 28.42 58.86
19
Mar 4, 2019		 GRN (single model)
Anonymous
52.92 66.71 52.37 84.11 31.77 58.47
20
Mar 1, 2019		 DFGN + BERT (single model)
Anonymous
55.17 68.49 49.85 81.06 31.87 58.23
21
Mar 4, 2019		 BERT Plus (single model)
CIS Lab
55.84 69.76 42.88 80.74 27.13 58.23
22
May 18, 2019		 KGNN (single model)
Anonymous
50.81 65.75 38.74 76.79 22.40 52.82
23
Oct 10, 2018		 Baseline Model (single model)
Carnegie Mellon University, Stanford University, & Universite de Montreal
(Yang, Qi, Zhang, et al. 2018)		 45.60 59.02 20.32 64.49 10.83 40.16
-
Sep 24, 2019		 ChainEx (single model)
UT Austin
61.20 74.11 N/A N/A N/A N/A
-
Feb 27, 2019		 DecompRC (single model)
University of Washington
(Min et al., ACL'18)		 55.20 69.63 N/A N/A N/A N/A
-
Apr 2, 2019		 MatrixRC (single model)
Anonymous
47.07 60.75 N/A N/A N/A N/A
Leaderboard (Fullwiki Setting)
In the fullwiki setting, a question-answering system must find the answer to a question in the scope of the entire Wikipedia. Similar to in the distractor setting, systems are evaluated on the accuracy of their answers (Ans) and the quality of the supporting facts they use to justify them (Sup).
Model Code Ans Sup Joint
EM F1 EM F1 EM F1
1
Oct 7, 2019		 HGN + SemanticRetrievalMRS IR (single model)
Microsoft Dynamics 365 AI Research
56.71 69.16 49.97 76.39 35.63 59.86
2
Sep 20, 2019		 Graph-based Recurrent Retriever (single model)
Anonymous
56.04 68.87 44.14 73.03 29.18 55.31
3
Sep 28, 2019		 MIR+EPS+BERT (single model)
Anonymous
52.86 64.79 42.75 72.00 31.19 54.75
4
Sep 21, 2019		 Transformer-XH (single model)
Anonymous
48.95 60.75 41.66 70.01 27.13 49.57
5
May 15, 2019		 SemanticRetrievalMRS (single model)
UNC-NLP
(Nie et al., EMNLP'2019)		 45.32 57.34 38.67 70.83 25.14 47.60
6
Jul 31, 2019		 Entity-centric BERT Pipeline (single model)
Anonymous
41.82 53.09 26.26 57.29 17.01 39.18
7
May 21, 2019		 GoldEn Retriever (single model)
Stanford University
(Qi et al., EMNLP-IJCNLP 2019)		 37.92 48.58 30.69 64.24 18.04 39.13
8
Aug 14, 2019		 PR-Bert (single model)
KingSoft AI Lab
43.33 53.79 21.90 59.63 14.50 39.11
9
Feb 21, 2019		 Cognitive Graph QA (single model)
Tsinghua KEG & Alibaba DAMO Academy
(Ding et al., ACL'19)		 37.12 48.87 22.82 57.69 12.42 34.92
10
Mar 5, 2019		 MUPPET (single model)
Technion
(Feldman and El-Yaniv, ACL'19)		 30.61 40.26 16.65 47.33 10.85 27.01
11
Apr 7, 2019		 GRN + BERT (single model)
Anonymous
29.87 39.14 13.16 49.67 8.26 25.84
12
May 20, 2019		 Entity-centric IR (single model)
Anonymous
35.36 46.26 0.06 43.16 0.02 25.47
13
May 19, 2019		 KGNN (single model)
Anonymous
27.65 37.19 12.65 47.19 7.03 24.66
14
Aug 16, 2019		 SAQA (single model)
Anonymous
28.44 38.62 14.69 47.17 8.62 24.49
15
Mar 4, 2019		 GRN (single model)
Anonymous
27.34 36.48 12.23 48.75 7.40 23.55
16
Nov 25, 2018		 QFE (single model)
NTT Media Intelligence Laboratories
(Nishida et al., ACL'19)
28.66 38.06 14.20 44.35 8.69 23.10
17
Oct 12, 2018		 Baseline Model (single model)
Carnegie Mellon University, Stanford University, & Universite de Montreal
(Yang, Qi, Zhang, et al. 2018)		 23.95 32.89 3.86 37.71 1.85 16.15
-
May 19, 2019		 TPReasoner w/o BERT (single model)
Anonymous
36.04 47.43 N/A N/A N/A N/A
-
Feb 28, 2019		 DecompRC (single model)
University of Washington
(Min et al., ACL'18)		 30.00 40.65 N/A N/A N/A N/A
-
Mar 3, 2019		 MultiQA (single model)
Anonymous
30.73 40.23 N/A N/A N/A N/A