Papers › Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and...
Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
Asma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman
This paper presents the MEDIQA 2019 shared task organized at the ACL-BioNLP workshop. The shared task is motivated by a need to develop relevant methods, techniques and gold standards for inference and entailment in the medical domain, and their application to improve domain specific information retrieval and question answering systems. MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain. 72 teams participated in the challenge, achieving an accuracy of 98{%} in the NLI task, 74.9{%} in the RQE task, and 78.3{%} in the QA task. In this paper, we describe the tasks, the datasets, and the participants{'} approaches and results. We hope that this shared task will attract further research efforts in textual inference, question entailment, and question answering in the medical domain.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections