Papers by Mingmin Wu
Refine, Align, and Aggregate: Multi-view Linguistic Features Enhancement for Aspect Sentiment Triplet Extraction (2024.findings-acl)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets of aspect terms, their associated sentiment and opinion terms. |
| Approach: | They propose to use multi-view linguistic features enhancement to explore the prior indication effect in the “Refine, Align, and Aggregate” learning process to enhance aspect-opinion relations. |
| Outcome: | The proposed model achieves state-of-the-art on several benchmark datasets and is robust to state- of-the art constraints. |
Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction (2025.coling-main)
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| Challenge: | Existing table-filling methods decompose the ASQP task into subtasks without considering the association between sentiment elements. |
| Approach: | They propose a simple yet effective Unified Grid Tagging Scheme to extract sentiment quadruplets in one shot . they leverage syntactic dependency tree and AMR graph to enrich association between sentiment elements . |
| Outcome: | The proposed model extracts all sentiment elements in quads for a given review to explain the reason for the sentiment. |
Refining Idioms Semantics Comprehension via Contrastive Learning and Cross-Attention (2024.lrec-main)
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| Challenge: | Existing methods based on deep learning struggle to grasp idiom semantics due to the figurative meanings of many idiomas deviating from their literal interpretations. |
| Approach: | They propose a Chinese idiom cloze test to capture comprehensive idiomatics and a semantic sense contrastive learning module to enhance the representation of idiomics. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Chinese idiom cloze test and on other benchmark datasets. |