| language: | |
| - en | |
| bigbio_language: | |
| - English | |
| license: unknown | |
| multilinguality: monolingual | |
| bigbio_license_shortname: UNKNOWN | |
| pretty_name: MEDIQA RQE | |
| homepage: https://sites.google.com/view/mediqa2019 | |
| bigbio_pubmed: False | |
| bigbio_public: True | |
| bigbio_tasks: | |
| - TEXT_PAIRS_CLASSIFICATION | |
| # Dataset Card for MEDIQA RQE | |
| ## Dataset Description | |
| - **Homepage:** https://sites.google.com/view/mediqa2019 | |
| - **Pubmed:** False | |
| - **Public:** True | |
| - **Tasks:** Text Pairs Classification | |
| The MEDIQA challenge is an ACL-BioNLP 2019 shared task aiming to attract further research efforts in Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and their applications in medical Question Answering (QA). | |
| Mailing List: https://groups.google.com/forum/#!forum/bionlp-mediqa | |
| The objective of the RQE task is to identify entailment between two questions in the context of QA. We use the following definition of question entailment: “a question A entails a question B if every answer to B is also a complete or partial answer to A” [1] | |
| [1] A. Ben Abacha & D. Demner-Fushman. “Recognizing Question Entailment for Medical Question Answering”. AMIA 2016. | |
| ## Citation Information | |
| ``` | |
| @inproceedings{MEDIQA2019, | |
| author = {Asma {Ben Abacha} and Chaitanya Shivade and Dina Demner{-}Fushman}, | |
| title = {Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering}, | |
| booktitle = {ACL-BioNLP 2019}, | |
| year = {2019} | |
| } | |
| ``` | |