Papers with MAMS

2 papers
From Graphs to Hypergraphs: Enhancing Aspect-Term Sentiment Analysis via Multi-Level Relational Modeling (2026.acl-srw)

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Challenge: Existing graph-based approaches to predict sentiment polarity for specific aspect terms rely on predefined pairwise structures to improve expressive capacity.
Approach: They propose a dynamic hypergraph framework that can be used to generate a single instance-specific hypergraph from contextual token representations.
Outcome: The proposed framework improves on Lap14, Rest14, and MAMS . it uses a single instance-specific hypergraph constructed directly from contextual token representations .
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis (D19-1)

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Challenge: Existing ABSA methods only use one aspect or multiple aspects with the same sentiment polarity . recent studies show that neural network methods can be trained end-to-end and automatically learn important features.
Approach: They propose a large-scale multi-aspect multi-sentiment dataset with two different aspects with different sentiment polarities.
Outcome: The proposed model outperforms the state-of-the-art models on the large-scale dataset . it is based on a novel neural network approach that can be trained end-to-end .

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