QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph

Abstract

In the era of Big Knowledge Graphs, Question Answering (QA) systems have reached a milestone in their performance and feasibility.However, their applicability, particularly in specific domains such as the biomedical domain, has not gained wide acceptance due to their "black box" nature, which hinders transparency, fairness, and accountability of QA systems.Therefore, users are unable to understand how and why particular questions have been answered, whereas some others fail.To address this challenge, in this paper, we develop an automatic approach for generating explanations during various stages of a pipeline-based QA system.Our approach is a supervised and automatic approach which considers three classes (i.e., success, no answer, and wrong answer) for annotating the output of involved QA components.Upon our prediction, a template explanation is chosen and integrated into the output of the corresponding component.To measure the effectiveness of the approach, we conducted a user survey as to how non-expert users perceive our generated explanations.The results of our study show a significant increase in the four dimensions of the human factor from the Human-computer interaction community.

Authors 3

1. Saeedeh Shekarpour

   University of Dayton

   Affiliation as printed

   University of Dayton Dayton , USA

   University of Dayton Dayton, USA

2. Abhishek Nadgeri Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen & Zerotha Research , Germany

   RWTH Aachen & Zerotha Research, Germany

3. Kuldeep Singh
   Affiliation as printed

   Cerence GmbH & Zerotha Research , Germany

   Cerence GmbH & Zerotha Research, Germany