EDUQA: Educational Domain Question Answering System using Conceptual Network Mapping

TL;DR

EDUQA introduces a conceptual network-based question answering system tailored for educational content, enhancing answer relevance by capturing pedagogical semantics and enabling interactive classroom learning.

Contribution

The paper presents a novel on-the-fly conceptual network model that incorporates educational semantics for improved answer generation in educational question answering.

Findings

Effective preservation of conceptual correlations improves answer accuracy.

Model facilitates interactive conversational agents for classroom learning.

Enhances pedagogical understanding over traditional textual comprehension models.

Abstract

Most of the existing question answering models can be largely compiled into two categories: i) open domain question answering models that answer generic questions and use large-scale knowledge base along with the targeted web-corpus retrieval and ii) closed domain question answering models that address focused questioning area and use complex deep learning models. Both the above models derive answers through textual comprehension methods. Due to their inability to capture the pedagogical meaning of textual content, these models are not appropriately suited to the educational field for pedagogy. In this paper, we propose an on-the-fly conceptual network model that incorporates educational semantics. The proposed model preserves correlations between conceptual entities by applying intelligent indexing algorithms on the concept network so as to improve answer generation. This model can be utilized for building interactive conversational agents for aiding classroom learning.