Epidemic Question Answering: question generation and entailment for Answer Nugget discovery.

Abstract

The rapidly growing body of communications during the COVID-19 pandemic posed a challenge to information seekers, who struggled to find answers to their specific and changing information needs. We designed a Question Answering (QA) system capable of answering ad-hoc questions about the COVID-19 disease, its causal virus SARS-CoV-2, and the recommended response to the pandemic. The QA system incorporates, in addition to relevance models, automatic generation of questions from relevant sentences. We relied on entailment between questions for (1) pinpointing answers and (2) selecting novel answers early in the list of its results. The QA system produced state-of-the-art results when processing questions asked by experts (eg, researchers, scientists, or clinicians) and competitive results when processing questions asked by consumers of health information. Although state-of-the-art models for question generation and question entailment were used, more than half of the answers were missed, due to the limitations of the relevance models employed. Although question entailment enabled by automatic question generation is the cornerstone of our QA system's architecture, question entailment did not prove to always be reliable or sufficient in ranking the answers. Question entailment should be enhanced with additional inferential capabilities. The QA system presented in this article produced state-of-the-art results processing expert questions and competitive results processing consumer questions. Improvements should be considered by using better relevance models and enhanced inference methods. Moreover, experts and consumers have different answer expectations, which should be accounted for in future QA development.