PQ-GCN: Enhancing Text Graph Question Classification with Phrase Features

TL;DR

This paper introduces PQ-GCN, a graph convolutional network that incorporates phrase features to improve question classification accuracy, especially in low-resource settings, offering a parameter-efficient alternative to large language models.

Contribution

The paper proposes a novel graph neural network model, PQ-GCN, that effectively integrates phrase features for enhanced question classification performance.

Findings

Outperforms baseline graph methods in low-resource scenarios

Competitive with large language models while using fewer parameters

Enhances context-awareness in question classification

Abstract

Effective question classification is crucial for AI-driven educational tools, enabling adaptive learning systems to categorize questions by skill area, difficulty level, and competence. It not only supports educational diagnostics and analytics but also enhances complex downstream tasks like information retrieval and question answering by associating questions with relevant categories. Traditional methods, often based on word embeddings and conventional classifiers, struggle to capture the nuanced relationships in question statements, leading to suboptimal performance. We propose a novel approach leveraging graph convolutional networks, named Phrase Question-Graph Convolutional Network (PQ-GCN). Through PQ-GCN, we evaluate the incorporation of phrase-based features to enhance classification performance on question datasets of various domains and characteristics. The proposed method, augmented with phrase-based features, outperform baseline graph-based methods in low-resource settings, and performs competitively against language model-based methods with a fraction of their parameter size. Our findings offer a possible solution for more context-aware, parameter-efficient question classification, bridging the gap between graph neural network research and its educational applications.