Optimizing Question Answering Systems in Education: Addressing Domain-Specific Challenges
Document Type
Article
Publication Title
IEEE Access
Abstract
Question Answering (QA) systems are increasingly essential in educational institutions, enhancing both learning and administrative processes by providing quick and accurate answers to user queries. However, existing systems often struggle with accurately classifying and responding to diverse and context-dependent questions, especially when dealing with large knowledge graphs. Predicting the domain of a question can significantly narrow down the search space within a vast knowledge graph, improving the system’s efficiency and accuracy. This study addresses this gap by developing and evaluating domain prediction models. We compare the performance of various deep learning architectures, including Bi-GRU, Bi-LSTM, GRU, and LSTM. Our results demonstrate that the 1-layer Bi-GRU model outperforms the others, achieving the highest test accuracy of 82.13%. Additionally, by employing an ensemble technique that combines models with highest performance measures from each architecture, we further enhance overall performance, achieving an accuracy of 87.14%, which demonstrates improved predictive capability. This work is significant as it provides a robust solution for improving the accuracy and relevance of QA systems in educational settings, thereby enhancing user satisfaction and operational efficiency.
First Page
156572
Last Page
156587
DOI
10.1109/ACCESS.2024.3483224
Publication Date
1-1-2024
Recommended Citation
Swathi, B. P.; Geetha, M.; Attigeri, Girija; and Suhas, M. V., "Optimizing Question Answering Systems in Education: Addressing Domain-Specific Challenges" (2024). Open Access archive. 10599.
https://impressions.manipal.edu/open-access-archive/10599