Papers with few-shot ABSA

2 papers
DS2-ABSA: Dual-Stream Data Synthesis with Label Refinement for Few-Shot Aspect-Based Sentiment Analysis (2025.acl-long)

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Challenge: Existing methods for annotating data are time-consuming and labor-intensive . Existing low-resource solutions comprise data augmentation and in-context learning .
Approach: They propose a dual-stream data synthesis framework for few-shot ABSA . it leverages key-point-driven and instance-driven LLMs to generate diverse data .
Outcome: Extensive experiments show that DS2-ABSA outperforms existing methods . previous studies have shown that the proposed approach generates diverse data .
In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis (2024.lrec-main)

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Challenge: Existing approaches to solve few-shot aspect-based sentiment analysis (ABSA) are suboptimal for this task because of in-context examples .
Approach: They propose to retrieve in-context examples for few-shot aspect-based sentiment analysis . they construct positive and negative pairs from three perspectives and train the retriever .
Outcome: The proposed retrieval framework outperforms baselines on four ABSA datasets.

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