René's URL Explorer Experiment


Title: HotpotQA Homepage

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

X Description: 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.

X: @qi2peng2

direct link

Domain: hotpotqa.github.io

Hey, it has json ld scripts: 
{
  "@context":"https://schema.org/",
  "@type":"Dataset",
  "name":"HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering",
  "description":"HotpotQA is a question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems built based on Wikipedia.",
  "url":"https://hotpotqa.github.io/",
  "keywords":[
     "Question Answering",
     "Natural Language Processing",
     "Multi-hop Reasoning",
     "Explainable Artificial Intelligence"
  ],
  "creator":{
     "@type":"Organization",
     "url": "https://hotpotqa.github.io/",
     "name":"Carnegie Mellon University, Stanford University, Université de Montréal",
     "contactPoint":{
        "@type":"ContactPoint",
        "contactType": "customer service",
        "url":"https://qipeng.me/",
        "email":"pengqi@cs.stanford.edu"
     }
  },
  "distribution":[
     {
        "@type":"DataDownload",
        "encodingFormat":"JSON",
        "contentUrl":"http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_train_v1.1.json"
     },
     {
        "@type":"DataDownload",
        "encodingFormat":"JSON",
        "contentUrl":"http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_distractor_v1.json"
     },
     {
        "@type":"DataDownload",
        "encodingFormat":"JSON",
        "contentUrl":"http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_fullwiki_v1.json"
     },
     {
        "@type":"DataDownload",
        "encodingFormat":"JSON",
        "contentUrl":"http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_test_fullwiki_v1.json"
     }
  ]
}
twitter:cardsummary_large_image
twitter:creator@qi2peng2
twitter:imagehttps://hotpotqa.github.io/img/question_types.png
og:imagehttps://hotpotqa.github.io/img/question_types.png
google-site-verificationh3zCtOJy9KzG1XWf5-3ppU1liaJ8NkpGvK_BbOOq3-4

Links:

HotpotQAhttps://hotpotqa.github.io/index.html
Bloghttps://hotpotqa.github.io/blog.html
Explorerhttps://hotpotqa.github.io/explorer.html
Carnegie Mellon Universityhttps://www.cs.cmu.edu/
Stanford Universityhttps://nlp.stanford.edu/
Université de Montréalhttps://diro.umontreal.ca/accueil/
(Yang, Qi, Zhang, et al. 2018)https://arxiv.org/pdf/1809.09600.pdf
BeerQAhttps://beerqa.github.io/
CC BY-SA 4.0 Licensehttp://creativecommons.org/licenses/by-sa/4.0/
Training set (535MB)http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_train_v1.1.json
Dev set (distractor) (44MB)http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_distractor_v1.json
Dev set (fullwiki) (45MB)http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_fullwiki_v1.json
Test set (fullwiki) (46MB)http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_test_fullwiki_v1.json
GitHub repositoryhttps://github.com/hotpotqa/hotpot
Getting started guidehttps://github.com/hotpotqa/hotpot/blob/master/README.md
Evaluation script (4.2KB)https://raw.githubusercontent.com/hotpotqa/hotpot/master/hotpot_evaluate_v1.py
Sample dev prediction (982KB)http://curtis.ml.cmu.edu/datasets/hotpot/sample_dev_pred.json
Submission Guidehttps://worksheets.codalab.org/worksheets/0xa8718c1a5e9e470e84a7d5fb3ab1dde2/
CC BY-SA 4.0 Licensehttp://creativecommons.org/licenses/by-sa/4.0/
Processed Wikipedia READMEhttps://hotpotqa.github.io/wiki-readme.html
Google grouphttps://groups.google.com/d/forum/hotpotqa
(Zhang, Zhang, Zhang, et al. 2023)https://arxiv.org/abs/2308.08973
https://github.com/canghongjian/beam_retriever
Rethinking Label Smoothing on Multi-hop Question Answeringhttps://arxiv.org/pdf/2212.09512.pdf
https://github.com/yinzhangyue/Smoothing-R3
From Easy to Hard: Two-stage Selector and Reader for Multi-hop Question Answeringhttps://arxiv.org/abs/2205.11729
https://github.com/weijunlei/hotpotqa_mrc
Rethinking Label Smoothing on Multi-hop Question Answeringhttps://arxiv.org/pdf/2212.09512.pdf
https://github.com/yinzhangyue/Smoothing-R3
(Alkhaldi et al., 2021)http://hdl.handle.net/2433/265879
(Shao, Cui et al. 2020)https://arxiv.org/abs/2004.03096
Tu, Huang et al., AAAI 2020https://arxiv.org/abs/1911.00484
Fang et al., 2019https://arxiv.org/abs/1911.03631
https://github.com/IBM/translucent-answer-prediction
https://github.com/IBM/translucent-answer-prediction
Tu, Huang et al., AAAI 2020https://arxiv.org/abs/1911.00484
(Xiao, Qu, Qiu et al. ACL19)https://arxiv.org/pdf/1905.06933.pdf
https://github.com/woshiyyya/DFGN-pytorch
(Nishida et al., ACL'19)https://arxiv.org/pdf/1905.08511.pdf
(Nishida et al., 2021)https://arxiv.org/abs/2111.09029
(Ye et al., 2019)https://arxiv.org/abs/1911.02170
(Yang, Qi, Zhang, et al. 2018)https://arxiv.org/pdf/1809.09600.pdf
https://github.com/hotpotqa/hotpot
Perez et al. EMNLP 2020https://arxiv.org/abs/2002.09758
https://github.com/facebookresearch/UnsupervisedDecomposition
(Chen et al., 2019)https://arxiv.org/pdf/1910.02610.pdf
(Min et al., ACL'18)https://arxiv.org/pdf/1906.02916.pdf
https://github.com/shmsw25/DecompRC
(Zhu, Pang et al., EMNLP 2021)https://arxiv.org/abs/2109.06747
https://github.com/zycdev/AISO
Ma et al. ACL 2023https://arxiv.org/abs/2305.03130
(Li, Li, Shang, et al. 2020)https://arxiv.org/abs/2012.15534
(Qi, Lee, Sido, and Manning. 2020)https://arxiv.org/abs/2010.12527
(Qi, Lee, Sido, and Manning. 2020)https://arxiv.org/abs/2010.12527
Xiong, Li et al., ICLR 2021https://arxiv.org/abs/2009.12756
https://github.com/facebookresearch/multihop_dense_retrieval
(Yuyu, Ping et al. 2020)https://arxiv.org/pdf/2009.07465.pdf
(Asai et al., ICLR 2020)https://arxiv.org/abs/1911.10470
https://github.com/AkariAsai/learning_to_retrieve_reasoning_paths
Fang et al., 2019https://arxiv.org/abs/1911.03631
A Simple Yet Strong Pipeline for HotpotQAhttps://www.semanticscholar.org/paper/A-Simple-Yet-Strong-Pipeline-for-HotpotQA-Groeneveld-Khot/c6c547e5ecb
(Zhao et al. ICLR 2020)https://openreview.net/pdf?id=r1eIiCNYwS
https://github.com/microsoft/Transformer-XH
(Nie et al., EMNLP'2019)http://arxiv.org/abs/1909.08041
https://github.com/easonnie/semanticRetrievalMRS
(Dhingra et al, ICLR 2020)https://openreview.net/forum?id=SJxstlHFPH
http://www.cs.cmu.edu/~bdhingra/pages/drkit.html
(Qi et al., EMNLP-IJCNLP 2019)https://arxiv.org/pdf/1910.07000.pdf
https://github.com/qipeng/golden-retriever
(Ding et al., ACL'19)https://arxiv.org/abs/1905.05460
https://github.com/THUDM/CogQA
(Feldman and El-Yaniv, ACL'19)https://arxiv.org/pdf/1906.06606
https://github.com/yairf11/MUPPET
(Ye et al., 2019)https://arxiv.org/abs/1911.02170
(Nishida et al., ACL'19)https://arxiv.org/pdf/1905.08511.pdf
(Yang, Qi, Zhang, et al. 2018)https://arxiv.org/pdf/1809.09600.pdf
https://github.com/hotpotqa/hotpot
Clean Bloghttps://startbootstrap.com/template-overviews/clean-blog/

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