GASTM: A Graph-Adapted Semantic Topic Model With Instance-Aware Dual-View Fusion

Abstract

In neural topic modeling, the reliability of a document representation varies from one document to the next: a syntactic view drawn from a dependency graph is informative when the parse is well formed, whereas a semantic view drawn from a pretrained transformer is more dependable when the parse is sparse or fragmented. Existing hybrid models combine the two views with a fixed rule, set once for the whole corpus through concatenation, averaging, or a constant weight, so the locally less reliable view is over-weighted on individual documents. To close these gaps, a Graph-Adapted Semantic Topic Model with Instance-Aware Dual-View Fusion (GASTM) is proposed. Within a variational autoencoder, a GGNN-based dependency encoder and a pretrained transformer semantic encoder are kept jointly trainable, while corpus-wide lexical statistics are carried through a learnable node embedding on the graph branch. A continuous per-document fusion weight is then produced by an instance-aware adaptive gate, allowing the two views to be combined in a controlled manner before reaching the latent inference space. On five benchmarks, GASTM is shown to consistently outperform the baselines on both topic quality and downstream classification. A fusion-type ablation further yields counterintuitive findings that support the effectiveness of the adaptive gate strategy over static alternatives.