Transition-based Opinion Generation for Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Existing approaches to aspect-based sentiment analysis often overlook the importance of explicitly modeling structure among sentiment elements. |
| Approach: | They propose to integrate general pre-trained sequence-to-sequence language models with a structure-aware transition-based approach to model sentiment structure. |
| Outcome: | The proposed model improves the state-of-the-art performance on several benchmark datasets. |
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| Challenge: | Existing generative models for aspect-based sentiment analysis lack structure well-formedness guarantees and built-in elements alignments. |
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| Challenge: | Recent advances in Aspect-Based Sentiment Analysis (ABSA) have shown promising results, yet the semantics derived solely from textual data remain limited. |
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| Challenge: | Existing attention-based models for sentiment analysis are not able to capture opinion spans as a whole or variable-length opinion span. |
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| Challenge: | Aspect-based sentiment analysis studies have focused on English datasets, but labeled data is scarce. |
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Label Correction Model for Aspect-based Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing models for aspect-based sentiment analysis ignore a phenomenon: aspect boundary label and sentiment label can correct each other. |
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