Recognizing Question Entailment in Consumer Health Using a Query Formulation Approach

EasyChair Preprint 9884

Abstract

The need for online assistance regarding healthcare has grown significantly; a deficiency which has become readily apparent after the advent of the SARS-COV-2/COVID-19 pandemic. A widespread, trusted means of dispersing the latest medical knowledge could have provided tremendous benefit from a public health standpoint and curtailed the spread of a disease which has claimed lives of millions. Question Answering (QA) systems are well-suited to provide this assistance for both medical professionals and the public at-large, especially considering the increased adoption in recent years of virtual digital assistants such as Samsung's Bixby and Google Assistant. The overall performance of QA systems can be improved by a variety of methods, including entailment-based methods. In this paper, we propose a Query-Based Framework for Recognizing Question Entailment (QBF-RQE), which leverages a query formulation method to identify whether two questions are in an entailment relationship - with a specific emphasis on Consumer Health Questions (CHQs). Our approach also incorporates type and focus features of CHQs to determine the entailment relationship. We evaluate our approach with the MEDIQA 2019 shared task organized at the ACL-BioNLP workshop. Our method gives 83.48%, while the best-performing model for MEDIQA 2019 was 74.9%.

Keyphrases: Consumer Health Question Answering, Health Informatics, Natural Language Inference, Recognizing Question Entailment, Recognizing Textual Entailment

@booklet{EasyChair:9884,
  author    = {Jooyeon Lee and Luan Pham and Ozlem Uzuner},
  title     = {Recognizing Question Entailment in Consumer Health Using a Query Formulation Approach},
  howpublished = {EasyChair Preprint 9884},
  year      = {EasyChair, 2023}}