Overview of the mediqa 2019 shared task on textual inference, question entailment and question answering

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Authors Asma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman
Journal/Conference Name Proceedings of the 18th BioNLP Workshop and Shared Task
Paper Category
Paper Abstract 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.
Date of publication 2019
Code Programming Language Python
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