Enhancing Long-Range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-Based Sentiment Analysis

TL;DR

This paper introduces MambaForGCN, a novel model combining syntax-based GCN, Mamba-Transformer, and Kolmogorov-Arnold Networks to improve long-range dependency modeling in aspect-based sentiment analysis, outperforming existing methods.

Contribution

It proposes a new architecture integrating dependency relations, semantic info, and adaptive feature fusion to enhance long-range dependency capture in ABSA.

Findings

Outperforms state-of-the-art models on three benchmark datasets.

Effectively captures long-range dependencies between aspect and opinion words.

Demonstrates robustness across different ABSA datasets.

Abstract

Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. However, attention mechanisms and neural network models struggle with syntactic constraints. The quadratic complexity of attention mechanisms also limits their adoption for capturing long-range dependencies between aspect and opinion words in ABSA. This complexity can lead to the misinterpretation of irrelevant contextual words, restricting their effectiveness to short-range dependencies. To address the above problem, we present a novel approach to enhance long-range dependencies between aspect and opinion words in ABSA (MambaForGCN). This approach incorporates syntax-based Graph Convolutional Network (SynGCN) and MambaFormer (Mamba-Transformer) modules to encode input with dependency relations and semantic information. The Multihead Attention (MHA) and Selective State Space model (Mamba) blocks in the MambaFormer module serve as channels to enhance the model with short and long-range dependencies between aspect and opinion words. We also introduce the Kolmogorov-Arnold Networks (KANs) gated fusion, an adaptive feature representation system that integrates SynGCN and MambaFormer and captures non-linear, complex dependencies. Experimental results on three benchmark datasets demonstrate MambaForGCN's effectiveness, outperforming state-of-the-art (SOTA) baseline models.