@inproceedings{wang-etal-2024-context,
    title = "In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis",
    author = "Wang, Qianlong  and
      Xu, Hongling  and
      Ding, Keyang  and
      Liang, Bin  and
      Xu, Ruifeng",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.786",
    pages = "8975--8985",
    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.",
}

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.
Wang, Qianlong Xu, Hongling Ding, Keyang Liang, Bin Xu, Ruifeng
In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis