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pith:WUAYVC6E

pith:2018:WUAYVC6ERFEWYXYELYQZXYMF63

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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Christopher D. Manning, Peng Qi, Ruslan Salakhutdinov, Saizheng Zhang, William W. Cohen, Yoshua Bengio, Zhilin Yang

HotpotQA introduces 113k Wikipedia questions that require multi-hop reasoning across documents along with sentence-level supporting facts for explanations.

arxiv:1809.09600 v1 · 2018-09-25 · cs.CL

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Claims

C1strongest claim

We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems' ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.

C2weakest assumption

That the questions genuinely require multi-hop reasoning over multiple documents rather than being answerable from single documents or surface patterns, and that the sentence-level supporting fact annotations are accurate and complete without introducing annotation biases.

C3one line summary

HotpotQA is a new dataset of 113k multi-hop Wikipedia questions with sentence-level supporting facts that enables training and evaluation of explainable QA systems.

References

19 extracted · 19 resolved · 0 Pith anchors

[1] Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading Wikipedia to answer open-domain questions. In Association for Computational Linguistics (ACL) 2017

[2] Christopher Clark and Matt Gardner. 2017. Simple and effective multi-paragraph reading comprehension. In Proceedings of the 55th Annual Meeting of the Association of Computational Linguistics 2017

[3] SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine 2017 · arXiv:1704.05179

[4] Weld, and Luke Zettlemoyer 2017

[5] Xiaodong Liu, Yelong Shen, Kevin Duh, and Jianfeng Gao. 2018. Stochastic answer networks for machine reading comprehension. In Proceedings of the 56th Annual Meeting of the Association for Computation 2018

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103 papers in Pith

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First computed	2026-07-04T23:04:43.013456Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b5018a8bc489496c5f045e219be185f6f9907413e8408851826702b1e42e138f

Aliases

arxiv: 1809.09600 · arxiv_version: 1809.09600v1 · doi: 10.48550/arxiv.1809.09600 · pith_short_12: WUAYVC6ERFEW · pith_short_16: WUAYVC6ERFEWYXYE · pith_short_8: WUAYVC6E

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/WUAYVC6ERFEWYXYELYQZXYMF63 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: b5018a8bc489496c5f045e219be185f6f9907413e8408851826702b1e42e138f

Canonical record JSON

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