Cited by 5Topic ModelingHealth Literacy and Information AccessibilityInformation Retrieval and Search Behavior

MedRedQA for Medical Consumer Question Answering: Dataset, Tasks, and Neural Baselines

Open access
Nguyen, Vincent, Karimi, Sarvnaz, Rybinski, Maciej, Xing, Zhenchang·2023·Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Abstract

Vincent Nguyen, Sarvnaz Karimi, Maciej Rybinski, Zhenchang Xing. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18653/v1/2023.ijcnlp-main.42

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 55

  1. Ben Abacha and Demner-Fushman. 2016. Recogniz- ing Question Entailment for Medical Question An- swering. American Medical Informatics Association Annual Symposium Proceedings, 2016:310-318.
  2. T. Armstrong, A. Moffat, W. Webber, and J. Zobel. 2009. Improvements that don't add up: Ad-hoc retrieval results since 1998. In CIKM, pages 601-610, Hong Kong, China. · DOI
  3. John W. Ayers, Adam Poliak, Mark Dredze, Eric C. Leas, Zechariah Zhu, Jessica B. Kelley, Dennis J. Faix, Aaron M. Goodman, Christopher A. Longhurst, Michael Hogarth, and Davey M. Smith. 2023. Com- paring Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Internal Medicine. · DOI · Paper page
  4. Marco Basaldella and Nigel Collier. 2019. BioRed- dit: Word embeddings for user-generated biomedical NLP. In LOUHI, pages 34-38, Hong Kong. · DOI
  5. Asma Ben Abacha and Dina Demner-Fushman. 2019. On the summarization of consumer health questions. In ACL, pages 2228-2234, Florence, Italy. · DOI
  6. Asma Ben Abacha and Dina Demner-Fushman. 2019. A question-entailment approach to question answering. CoRR, abs/1901.08079. · arXiv
  7. Asma Ben Abacha, Chaitanya Shivade, and Dina Demner-Fushman. 2019. Overview of the MEDIQA 2019 shared task on textual inference, question en- tailment and question answering. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 370-379, Florence, Italy. · DOI
  8. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. CoRR, abs/2005.14165. · arXiv
  9. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Al- bert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdh- ery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Ja- cob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022. Scaling instruction-finetuned language models. CoRR, abs/2210.11416. · arXiv
  10. Anthony Cocco, Rachel Zordan, David Taylor, Tracey Weiland, Stuart Dilley, Joyce Kant, Mahesha Dom- bagolla, Andreas Hendarto, Fiona Lai, and Jennie Hutton. 2018. Dr Google in the ED: searching for online health information by adult emergency depart- ment patients. The Medical Journal of Australia, 209:342-347. · DOI · Paper page
  11. Dina Demner-Fushman, Yassine Mrabet, and Asma Ben Abacha. 2020. Consumer health information and question answering: helping consumers find answers to their health-related information needs. JAMIA, 27(2):194-201. 31592532[pmid. · DOI
  12. Renee Garett and Sean D Young. 2021. Online mis- information and vaccine hesitancy. Transl. Behav. Med., 11(12):2194-2199.
  13. Suzanne Graham and John Brookey. 2008. Do patients understand? The Permanente journal, 12(3):67-69. · DOI
  14. Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2021. Domain-specific lan- guage model pretraining for biomedical natural lan- guage processing. ACM Trans. Comput. Healthcare, 3(1). · DOI · Paper page
  15. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasu- pat, and Ming-Wei Chang. 2020. Realm: Retrieval- augmented language model pre-training. In ICML, Online. · DOI
  16. Matthew L. Henderson, Rami Al-Rfou, Brian Strope, Yun-Hsuan Sung, László Lukács, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, and Ray Kurzweil. 2017. Effi- cient natural language response suggestion for smart reply. CoRR, abs/1705.00652. · arXiv
  17. Azhar Hussain, Syed Ali, Madiha Ahmed, and She- haryar Hussain. 2018. The anti-vaccination move- ment: A regression in modern medicine. Cureus, 10(7):e2919-e2919. PMC6122668[pmcid]. · DOI
  18. Paul Jaccard. 1912. The distribution of the flora in the alpine zone.1. New Phytologist, 11:37-50. · DOI · Paper page
  19. Jimmy, Guido Zuccon, Bevan Koopman, and Gianluca Demartini. 2019. Health card retrieval for consumer health search: An empirical investigation of methods. In CIKM, page 2405-2408, New York, NY, USA. · DOI
  20. Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019. PubMedQA: A dataset for biomedical research question answering. In EMNLP-IJCNLP, pages 2567-2577, Hong Kong, China. · DOI · Paper page
  21. Alistair Johnson, Tom Pollard, Lu Shen, Li-wei Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Anthony Celi, and Roger Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific Data, 3(1):160035. · DOI · Paper page
  22. Roozenbeek Jon, Schneider Claudia, Dryhurst Sarah, Kerr John, Freeman Alexandra, Recchia Gabriel, van der Bles Anne Marthe, and van der Lin- den Sander. 2020. Susceptibility to misinformation about covid-19 around the world. R. Soc. Open. Sci., 7. · DOI · Paper page
  23. Rabeeh Karimi Mahabadi, Luke Zettlemoyer, James Henderson, Lambert Mathias, Marzieh Saeidi, Veselin Stoyanov, and Majid Yazdani. 2022. Prompt- free and efficient few-shot learning with language models. In ACL, pages 3638-3652, Dublin, Ireland. · DOI
  24. Anastasia Krithara, Anastasios Nentidis, Georgios Paliouras, and Ioannis Kakadiaris. 2016. Results of the 4th edition of BioASQ challenge. In Proceedings of the Fourth BioASQ workshop, pages 1-7, Berlin, Germany. · DOI
  25. Bridget M. Kuehn. 2013. More Than One-Third of US Individuals Use the Internet to Self-diagnose. Jour- nal of the American Medical Association, 309(8):756- 757. · DOI
  26. Chin-Yew Lin. 2004. ROUGE: A package for auto- matic evaluation of summaries. In ACL, pages 74-81, Barcelona, Spain.
  27. Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mo- hta, Tenghao Huang, Mohit Bansal, and Colin A Raf- fel. 2022a. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. In NIPS, volume 35, pages 1950-1965.
  28. Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022b. What makes good in-context examples for GPT-3? In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extrac- tion and Integration for Deep Learning Architectures, pages 100-114, Dublin, Ireland and Online. · DOI · Paper page
  29. Ilya Loshchilov and Frank Hutter. 2017. Decoupled Weight Decay Regularization. In ICLR, Toulon, France.
  30. Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In NIPS, pages 4765-4774.
  31. Taylor Nelson, Nicole Kagan, Claire Critchlow, Alan Hillard, and Albert Hsu. 2020. The danger of mis- information in the COVID-19 crisis. Mo. Med., 117(6):510-512.
  32. Anastasios Nentidis, Konstantinos Bougiatiotis, Anasta- sia Krithara, Georgios Paliouras, and Ioannis Kakadi- aris. 2017. Results of the fifth edition of the bioasq challenge. In Biomedical Natural Language Process- ing, pages 48-57, Vancouver, Canada. · DOI
  33. Anastasios Nentidis, Georgios Katsimpras, Eirini Vandorou, Anastasia Krithara, Antonio Miranda- Escalada, Luis Gasco, Martin Krallinger, and Geor- gios Paliouras. 2022. Overview of BioASQ 2022: The tenth bioasq challenge on large-scale biomedi- cal semantic indexing and question answering. In Experimental IR Meets Multilinguality, Multimodal- ity, and Interaction, pages 337-361, Cham. Springer International Publishing. · DOI
  34. Anastasios Nentidis, Anastasia Krithara, Konstantinos Bougiatiotis, Georgios Paliouras, and Ioannis Kakadi- aris. 2018. Results of the sixth edition of the BioASQ challenge. In Proceedings of the 6th BioASQ Work- shop A challenge on large-scale biomedical semantic indexing and question answering, pages 1-10, Brus- sels, Belgium.
  35. Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. In NIPS Cognitive Computations Workshop, volume 1773, Barcelona, Spain.
  36. Vincent Nguyen, Sarvnaz Karimi, and Zhenchang Xing. 2019. ANU-CSIRO at MEDIQA 2019: Question answering using deep contextual knowledge. In Pro- ceedings of the 18th BioNLP Workshop and Shared Task, pages 478-487, Florence, Italy. · DOI
  37. Vincent Nguyen, Maciej Rybinski, Sarvnaz Karimi, and Zhenchang Xing. 2022. Search like an expert: Reduc- ing expertise disparity using a hybrid neural index for covid-19 queries. Journal of Biomedical Informatics, 127:104005. · DOI
  38. Rodrigo Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. 2019. Multi-stage document ranking with BERT. CoRR, abs/1910.14424. · arXiv
  39. Nils Reimers and Iryna Gurevych. 2019. Sentence- BERT: Sentence embeddings using Siamese BERT- networks. In EMNLP, pages 3982-3992, Hong Kong, China. · DOI · Paper page
  40. Kirk Roberts, M. Simpson, Dina Demner-Fushman, Ellen Voorhees, and William Hersh. 2016. State-of- the-art in biomedical literature retrieval for clinical cases: A survey of the TREC 2014 CDS track. Inf Retr., 19(1-2):113-148. · DOI
  41. Noam Shazeer and Mitchell Stern. 2018. Adafactor: Adaptive learning rates with sublinear memory cost. CoRR, abs/1804.04235. · arXiv
  42. Chaitanya Shivade. 2019. Mednli -a natural language inference dataset for the clinical domain (version 1.0.0. PhysioNet. · DOI
  43. Yuanhe Tian, Weicheng Ma, Fei Xia, and Yan Song. 2019. ChiMed: A Chinese medical corpus for ques- tion answering. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 250-260, Florence, Italy. · DOI
  44. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. CoRR, abs/2302.13971. · arXiv
  45. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, and I. Polosukhin. 2017. Attention is all you need. In NIPS, pages 6000-6010.
  46. Ellen Voorhees, Alam Tasmeer, Demner-Fushman Dina, Hersh William, and Kyle Lo. 2020. TREC-COVID: Constructing a pandemic information retrieval test collection. ACM SIGIR Forum, 54(1):1-12. · DOI
  47. Somin Wadhwa, Vivek Khetan, Silvio Amir, and By- ron Wallace. 2023. Redhot: A corpus of annotated medical questions, experiences, and claims on social media. In EACL: Findings.
  48. Shuai Wang, Shengyao Zhuang, and Guido Zuccon. 2021. BERT-based dense retrievers require interpo- lation with bm25 for effective passage retrieval. In ICTIR, page 317-324, New York, NY, USA. · DOI
  49. Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2022. Finetuned language models are zero-shot learners. In ICLR, Online.
  50. Dirk Weißenborn, George Tsatsaronis, and Michael Schroeder. 2013. Answering factoid questions in the biomedical domain. In CEUR Workshop Pro- ceedings, volume 1094, Valencia, Spain.
  51. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, and Jamie Brew. 2019. HuggingFace's Transformers: State-of-the-art Natural Language Processing. CoRR, page arXiv:1910.03771. · arXiv
  52. Hamed Zamani, Mostafa Dehghani, W. Bruce Croft, Erik Learned-Miller, and Jaap Kamps. 2018. From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing. In CIKM, page 497-506, Torino, Italy. · DOI
  53. Sheng Zhang, Xin Zhang, Hui Wang, Lixiang Guo, and Shanshan Liu. 2018. Multi-scale attentive interac- tion networks for chinese medical question answer selection. IEEE Access, 6:74061-74071. · DOI
  54. Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Chris- tian M. Meyer, and Steffen Eger. 2019. Moverscore: Text generation evaluating with contextualized em- beddings and earth mover distance. In EMNLP, Hong Kong, China.
  55. Ming Zhu, Aman Ahuja, Da-Cheng Juan, Wei Wei, and Chandan K. Reddy. 2020. Question answering with long multiple-span answers. In EMNLP (Findings), pages 3840-3849, Online. · DOI

Cited by

Sorted by citations

Authors
Nguyen, V., Karimi, S., Rybinski, M., & Xing, Z.
Affiliations
  • Australian National University (AU)
  • Commonwealth Scientific and Industrial Research Organisation (AU)
Venue
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
Published
2023
Pages
629-648
OpenAlex
W4392637261
Cited by
5
References
7
Citations by year
2026: 2 · 2025: 3
FWCI
0.89
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Keywords
medical question answering, consumer health information, question answering dataset, medical text understanding

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review