Year: 2025
Type: conference-paper
Abstract: Topic modeling plays a vital role in uncovering hidden semantic structures within text corpora, but existing models struggle in lowresource settings where limited target-domain data leads to unstable and incoherent topic inference.We address this challenge by formally introducing domain adaptation for lowresource topic modeling, where a high-resource source domain informs a low-resource target domain without overwhelming it with irrelevant content.We establish a finite-sample generalization bound showing that effective knowledge transfer depends on robust performance in both domains, minimizing latent-space discrepancy, and preventing overfitting to the data.Guided by these insights, we propose DALTA (Domain-Aligned Latent Topic Adaptation), a new framework that employs a shared encoder for domain-invariant features, specialized decoders for domain-specific nuances, and adversarial alignment to selectively transfer relevant information.Experiments on diverse lowresource datasets demonstrate that DALTA consistently outperforms state-of-the-art methods in terms of topic coherence, stability, and transferability.
Authors: Pritom Saha Akash, Kevin Chen-Chuan Chang
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G3668672548, BIGDATA: F: Bringing Interactive Data Management to Scientists, Analysts, and the Masses: A Holistic Unification of Spreadsheets and Databases, III: Small: Social Discovery of Users and Content in Social Media Through Similarity-Based and Graph-Based Inference of Attributes and Queries
OpenAlex ID: W4412945684