Hongling Xu


2024

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Improving In-Context Learning with Prediction Feedback for Sentiment Analysis
Hongling Xu | Qianlong Wang | Yice Zhang | Min Yang | Xi Zeng | Bing Qin | Ruifeng Xu
Findings of the Association for Computational Linguistics: ACL 2024

Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning (ICL) paradigm. However, their ability to distinguish subtle sentiments still remains a challenge. Inspired by the human ability to adjust understanding via feedback, this paper enhances ICL by incorporating prior predictions and feedback, aiming to rectify sentiment misinterpretation of LLMs. Specifically, the proposed framework consists of three steps: (1) acquiring prior predictions of LLMs, (2) devising predictive feedback based on correctness, and (3) leveraging a feedback-driven prompt to refine sentiment understanding. Experimental results across nine sentiment analysis datasets demonstrate the superiority of our framework over conventional ICL methods, with an average F1 improvement of 5.95%.

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HITSZ-HLT at SIGHAN-2024 dimABSA Task: Integrating BERT and LLM for Chinese Dimensional Aspect-Based Sentiment Analysis
Hongling Xu | Delong Zhang | Yice Zhang | Ruifeng Xu
Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)

This paper presents the winning system participating in the ACL 2024 workshop SIGHAN-10 shared task: Chinese dimensional aspect-based sentiment analysis (dimABSA). This task aims to identify four sentiment elements in restaurant reviews: aspect, category, opinion, and sentiment intensity evaluated in valence-arousal dimensions, providing a concise yet fine-grained sentiment description for user opinions. To tackle this task, we introduce a system that integrates BERT and large language models (LLM) to leverage their strengths. First, we explore their performance in entity extraction, relation classification, and intensity prediction. Based on preliminary experiments, we develop an integrated approach to fully utilize their advantages in different scenarios. Our system achieves first place in all subtasks and obtains a 41.7% F1-score in quadruple extraction.

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In-Context Example Retrieval from Multi-Perspectives for Few-Shot Aspect-Based Sentiment Analysis
Qianlong Wang | Hongling Xu | Keyang Ding | Bin Liang | Ruifeng Xu
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

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.