GHI: Graphormer over Conditioned Hypergraph Incidence for Aspect-Based Sentiment Analysis
For researchers in fine-grained sentiment analysis, GHI shows that compact structural reasoning can rival large-scale models, offering a parameter-efficient alternative.
GHI introduces a Graphormer-over-Conditioned-Hypergraph-Incidence framework for aspect-based sentiment analysis, outperforming all baselines on SemEval domains and approaching 11B Flan-T5 performance with only 247M parameters on the ISE benchmark.
Aspect-based sentiment analysis (ABSA) requires models to bind sentiment evidence to the correct aspect, making it a natural testbed for fine-grained structural reasoning. We introduce GHI, a Graphormer-over-Conditioned-Hypergraph-Incidence framework that is designed as an incidence-based structural reasoning layer built on a bipartite topology. GHI represents diverse linguistic and semantic evidence as token--hyperedge incidence relations, allowing different structural signals to be incorporated through a unified interface. Extensive experiments on six standard ABSA benchmarks show that GHI outperforms all baselines on the SemEval domains, and multi-seed evaluations show stable improvements over strong DeBERTa. Further experiments show that with only 247M parameters, GHI approaches the performance of 11B Flan-T5 based methods on the ISE benchmark. Moreover, it demonstrates strong robustness on the challenging ARTS datasets, maintaining highly competitive performance where traditional models degrade. These results demonstrate that compact structural reasoning remains a valuable alternative to scale-driven approaches for fine-grained tasks.