Refining and Synthesis: A Simple yet Effective Data Augmentation Framework for Cross-Domain Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) data augmentation has attracted increasing attention in recent years due to data sparsity. |
| Approach: | They propose a framework to augment ABSA data using pseudo labels for target domain . they refine generated labeled data using a natural language inference filter . |
| Outcome: | The proposed framework outperforms 7 strong baselines on 4 kinds of ABSA tasks. |
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| Challenge: | Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect-sentiment pairs in sentences from a target domain. |
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a task of analyzing people's sentiments at the aspect level. |
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| Challenge: | Existing methods to train ABSA model are limited by lack of annotated data . a dual-granularity pseudo labeling approach is proposed to solve this problem . |
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| Challenge: | Existing work on Aspect-based sentiment analysis ignores the rich label semantics of ABSA. |
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