CMV-Fuse: Cross Modal-View Fusion of AMR, Syntax, and Knowledge Representations for Aspect Based Sentiment Analysis

TL;DR

CMV-Fuse is a novel framework that integrates multiple linguistic perspectives, including semantic representations, syntax, and external knowledge, to improve aspect-based sentiment analysis by emulating human language understanding.

Contribution

It introduces a hierarchical fusion and contrastive learning approach to combine diverse linguistic views, enhancing sentiment analysis performance beyond existing methods.

Findings

Significant performance improvements on benchmark datasets.

Effective integration of multiple linguistic perspectives.

Insights into the contribution of each view to sentiment analysis.

Abstract

Natural language understanding inherently depends on integrating multiple complementary perspectives spanning from surface syntax to deep semantics and world knowledge. However, current Aspect-Based Sentiment Analysis (ABSA) systems typically exploit isolated linguistic views, thereby overlooking the intricate interplay between structural representations that humans naturally leverage. We propose CMV-Fuse, a Cross-Modal View fusion framework that emulates human language processing by systematically combining multiple linguistic perspectives. Our approach systematically orchestrates four linguistic perspectives: Abstract Meaning Representations, constituency parsing, dependency syntax, and semantic attention, enhanced with external knowledge integration. Through hierarchical gated attention fusion across local syntactic, intermediate semantic, and global knowledge levels, CMV-Fuse captures both fine-grained structural patterns and broad contextual understanding. A novel structure aware multi-view contrastive learning mechanism ensures consistency across complementary representations while maintaining computational efficiency. Extensive experiments demonstrate substantial improvements over strong baselines on standard benchmarks, with analysis revealing how each linguistic view contributes to more robust sentiment analysis.