SOURCE-LINKED INTELLIGENCE

The Impact of Synthetic Data Augmentation on Discourse-Pragmatic Function Classification

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Synthetic data augmentation has become a common strategy for addressing class imbalance in NLP, but most approaches focus on the quantity and diversity of generated examples rather than their geometric relationship to real training data. We investigate this question in the context of discourse pragmatic function classification, a task where data sparsity is a structural feature rather than a collection artefact. Using 410 manually annotated instances of the English word look drawn from the British National Corpus, spanning four functions: Attention Signal, Directive, Discourse Marker, and Inte

Read original source ↗ Open in workspace

recordType

paper

region

Global