QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph
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.
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessKnowledge GraphsQuestion AnsweringSimilar Papers 제목 키워드 기반
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention
In this paper, we present a novel approach for the task of eXplainable Question Answering (XQA), i.e., generating natural language (NL) explanations for the Visual Question Answering (VQA) problem. We generate NL explana…
Explanation GenerationLanguage ModelingLanguage ModellingQuestion Answering+2MedExQA: Medical Question Answering Benchmark with Multiple Explanations
This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models' (LLMs) understanding of medical knowledge through explanations. By constructing datasets across five dist…
Medical Question AnsweringQuestion AnsweringVariational Causal Inference Network for Explanatory Visual Question Answering
Explanatory Visual Question Answering (EVQA) is a recently proposed multimodal reasoning task that requires answering visual questions and generating multimodal explanations for the reasoning processes. Unlike tradit…
Explanation GenerationExplanatory Visual Question AnsweringFS-MEVQAMultimodal Reasoning+3Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models
Attention maps, a popular heatmap-based explanation method for Visual Question Answering (VQA), are supposed to help users understand the model by highlighting portions of the image/question used by the model to infer an…
Question AnsweringVisual Question AnsweringVisual Question Answering (VQA)CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
Providing explanations in the context of Visual Question Answering (VQA) presents a fundamental problem in machine learning. To obtain detailed insights into the process of generating natural language explanations for VQ…
Explanation GenerationQuestion AnsweringText GenerationVisual Question Answering+2