MEGA: xLSTM with Multihead Exponential Gated Fusion for Precise Aspect-based Sentiment Analysis

• URL: http://arxiv.org/abs/2507.01213v1
• Date: Tue, 01 Jul 2025 22:21:33 GMT
• Title: MEGA: xLSTM with Multihead Exponential Gated Fusion for Precise Aspect-based Sentiment Analysis
• Authors: Adamu Lawan, Juhua Pu, Haruna Yunusa, Jawad Muhammad, Muhammad Lawan,
• Abstract summary: Aspect-based Sentiment Analysis (ABSA) is a critical Natural Language Processing (NLP) task that extracts aspects from text and determines their associated sentiments.<n>Existing ABSA methods struggle to balance computational efficiency with high performance.<n>We propose xLSTM with Multihead Exponential Gated Fusion (MEGA), a novel framework integrating a bi-directional mLSTM architecture with forward and partially flipped backward streams.
• Score: 2.9045498954705886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect-based Sentiment Analysis (ABSA) is a critical Natural Language Processing (NLP) task that extracts aspects from text and determines their associated sentiments, enabling fine-grained analysis of user opinions. Existing ABSA methods struggle to balance computational efficiency with high performance: deep learning models often lack global context, transformers demand significant computational resources, and Mamba-based approaches face CUDA dependency and diminished local correlations. Recent advancements in Extended Long Short-Term Memory (xLSTM) models, particularly their efficient modeling of long-range dependencies, have significantly advanced the NLP community. However, their potential in ABSA remains untapped. To this end, we propose xLSTM with Multihead Exponential Gated Fusion (MEGA), a novel framework integrating a bi-directional mLSTM architecture with forward and partially flipped backward (PF-mLSTM) streams. The PF-mLSTM enhances localized context modeling by processing the initial sequence segment in reverse with dedicated parameters, preserving critical short-range patterns. We further introduce an mLSTM-based multihead cross exponential gated fusion mechanism (MECGAF) that dynamically combines forward mLSTM outputs as query and key with PF-mLSTM outputs as value, optimizing short-range dependency capture while maintaining global context and efficiency. Experimental results on three benchmark datasets demonstrate that MEGA outperforms state-of-the-art baselines, achieving superior accuracy and efficiency in ABSA tasks.

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