EDUQA: Educational Domain Question Answering System using Conceptual Network Mapping
This work addresses the need for better educational tools for students and teachers by developing a domain-specific system for classroom learning, though it appears incremental as it builds on existing QA methods with added semantics.
The paper tackles the problem of existing question answering models lacking pedagogical understanding for educational use by proposing a conceptual network model that incorporates educational semantics, resulting in improved answer generation through intelligent indexing on concept networks.
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.