Abstract
Accurate detection of semantically similar texts underpins applications such as plagiarism detection and content recommendation, yet remains challenging in low-resource languages like Urdu due to limited labeled data and noisy annotations. We introduce Siamese Graph-Based Semi-Supervised Learning (SGSSL), a framework that expands training diversity through back-translation and masked-word prediction, and learns from unlabeled data via confidence-aware pseudo-labeling with consistency regularization. At its core, SGSSL employs a Siamese Graph Neural Network that fuses Graph Convolutional Networks and Graph Attention Networks to capture fine-grained relational signals between sentences. A bottleneck feature-refinement layer and a Transformer Encoder with multi-head self-attention mitigate GNN over-smoothing while modeling long-range dependencies. We further leverage Urdu RoBERTa embeddings for deep contextual semantics. Our hybrid confidence-based filtering integrates probabilistic confidence, semantic similarity, and model uncertainty to select high-quality pseudo-labels. Evaluated on six benchmark datasets for text reuse and paraphrase detection (binary and ternary classification), SGSSL consistently outperforms strong baselines. On the USTRC dataset, SGSSL attains an accuracy of 0.722, an 8.1% absolute gain over the best baseline, demonstrating effectiveness for low-resource settings.