Papers with MAST

2 papers
MAST: A Multi-View Alignment Strategy for Optimal Transport-Based Contrastive Clustering of Short Text (2026.findings-acl)

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Challenge: Short text clustering has gained significant prominence due to its ubiquity in real-world applications.
Approach: They propose a multi-view alignment strategy with transport-based clustering that integrates structural views to capture multi-granularity semantic features.
Outcome: Experiments show that MAST outperforms state-of-the-art methods on benchmark datasets.
The Multimodal Annotation Software Tool (MAST) (2022.lrec-1)

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Challenge: Existing visual and multimodal annotation systems are limited in scope and applicability.
Approach: They propose a tool that allows users to analyze visual and multimodal documents . they aim to provide a powerful and innovative annotation tool with application across fields .
Outcome: The MAST tool allows users to analyze visual and multimodal documents . it allows annotation theories to be citable, while evolving and being shared .

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