MEDIQA 2019

Textual Inference and Question Entailment in the Medical Domain 

MEDIQA is a series of shared tasks on Medical NLP & AI.

Introduction

The MEDIQA 2019 challenge aims to attract further research efforts in Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and their applications in medical Question Answering (QA). This ACL-BioNLP 2019 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 IR and QA systems (Overview Paper).    

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Tasks 

1) NLI: This first task consists in identifying three inference relations between two sentences: Entailment, Neutral and Contradiction  [1]   


2) RQE: This task focuses on identifying 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" [2]   


3) QA: The objective of this task is to filter and improve the ranking of automatically retrieved answers. The input ranks are generated by the medical QA system CHiQA. We highly recommend the reuse of RQE and/or NLI systems (first tasks) in the QA task [3-5]


Organizers

Important Dates 

Data & Evaluation 

** All datasets and evaluation scripts are available at : https://github.com/abachaa/MEDIQA2019 [6]

Training sets: 

 In addition, the MedQuAD dataset of 47k question-answer pairs can be used to retrieve answered questions that are entailed from the original questions [3].  

Validation and test sets: 

Evaluation measures: Accuracy for the NLI and RQE tasks. For the QA task: Mean Reciprocal Rank (MRR), Accuracy, Precision, and Spearman's Rank Correlation Coefficient. 

Results:


References  

[1] A. Romanov & C. Shivade. Lessons from Natural Language Inference in the Clinical Domain. EMNLP 2018.  DATA

[2] A. Ben Abacha & D. Demner-Fushman. Recognizing Question Entailment for Medical Question Answering. AMIA 2016.  DATA

[3] A. Ben Abacha & D. Demner-Fushman. A Question-Entailment Approach to Question Answering. arXiv:1901.08079 [cs.CL], January 2019. DATA

[4] S. Harabagiu & A. Hickl. Methods for using textual entailment in open-domain question answering. ACL 2006.  

[5] A. Ben Abacha, E. Agichtein, Y. Pinter & D. Demner-Fushman. Overview of the Medical Question Answering Task at TREC 2017 LiveQA. TREC 2017. DATA

[6] A. Ben Abacha, C. Shivade, and D. Demner-Fushman. Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering. ACL-BioNLP 2019.