A Convolutionaly-Informed XLM-RoBERTa for Classifying Short Text Inquires in Greek School Network

This work presents a novel classification model aimed at automatically categorizing user inquiries submitted to the helpdesk unit of the Greek School Network (GSN).Addressing the need for robust and accurate classification systems, a model architecture that combines a pre-trained XLM-Roberta (XLM-R) with Temporal Convolutional Network (TCN) and Transformer layers is proposed.The proposed model operates over binary-class structures and is evaluated on the GSN dataset, a dataset that includes labeled user queries.This is a first set of experimental results in an ongoing investigation on GSN data, where our proposed scheme is being compared to a total of seventeen classification schemes that include both traditional machine learning models and other transformer-based architectures.Results indicate that the LM-R TCN Transformer outperforms the alternatives, achieving an accuracy of 0.94 with similarly high precision, recall, and F1 scores, while also demonstrating robust inter-annotator agreement via Kappa and MCC scores of 0.82.Our findings suggest that the proposed architecture offers a strong and reliable solution for text classification tasks in the specific GSN context, particularly for our low-resource Greek language framework.The latter also highlights the broader applicability of transformer-based models in educational service domains.