In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis
Qianlong Wang, Hongling Xu, Keyang Ding, Bin Liang, Ruifeng Xu
Abstract
In this paper, we focus on few-shot aspect-based sentiment analysis (ABSA) and try to solve it with in-context learning (ICL) paradigm. However, the effectiveness of ICL is highly affected by retrieved in-context examples. Previous works generally leverage the semantic similarity between the candidate examples and test input to retrieve examples. However, they may yield sub-optimal results for this task. This is because considering only the overall semantic perspective may leave some useful examples, which have syntactic structural relevance to the test input or share identical sentiments and similar aspects to one unretrievable. To address this shortcoming, we advocate retrieving in-context examples for few-shot ABSA by simultaneously considering three perspectives, overall semantics, syntactic structure relevance, and aspect-sentiment semantics. To achieve this, we construct positive and negative pairs from these three perspectives and train the demonstration retriever using contrastive learning. Experimental results on four ABSA datasets show that our retrieval framework can significantly outperform baselines across the board. Moreover, to understand factors influencing ICL performance on few-shot ABSA, we conduct extensive analysis in various scenarios, which can inspire and advance future research.- Anthology ID:
- 2024.lrec-main.786
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 8975–8985
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.786
- DOI:
- Cite (ACL):
- Qianlong Wang, Hongling Xu, Keyang Ding, Bin Liang, and Ruifeng Xu. 2024. In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8975–8985, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis (Wang et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.lrec-main.786.pdf