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={Proceedings of the 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
	Model	Code	EM	F₁	EM	F₁	EM	F₁
1 Nov 21, 2018	QFE (single model) NTT Media Intelligence Laboratories		53.86	68.06	57.75	84.49	34.63	59.61
2 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

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
	Model	Code	EM	F₁	EM	F₁	EM	F₁
1 Nov 25, 2018	QFE (single model) NTT Media Intelligence Laboratories		28.66	38.06	14.20	44.35	8.69	23.10
2 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