Papers by Keyang Ding
Reducing Spurious Correlations in Aspect-based Sentiment Analysis with Explanation from Large Language Models (2023.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis models are susceptible to learning spurious correlations between words . a recent study shows that feature engineering is time-consuming and costly . |
| Approach: | They propose to use a template to prompt LLMs to generate an appropriate explanation for the sentiment polarity of each aspect to reduce spurious correlations. |
| Outcome: | The proposed methods improve ABSA models and their generalization ability. |
Error Comparison Optimization for Large Language Models on Aspect-Based Sentiment Analysis (2025.acl-long)
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| Challenge: | Existing methods for aspect-based sentiment analysis (ABSA) only compare current predictions and labels on each sample, yet fail to perceive and understand its error outputs from different degrees. |
| Approach: | They propose a framework that can perceive and understand the degree of errors by learning from comparative error pairs. |
| Outcome: | The proposed framework exceeds baselines and achieves the desired performance. |
Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics (2020.emnlp-main)
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| Challenge: | A large-scale dataset is collected from Chinese microblog Sina Weibo with over 13 thousand trending topics, emotion votes in 24 fine-grained types from massive participants, and user comments to allow context understanding. |
| Approach: | They use a large-scale dataset from Chinese microblog Sina Weibo to examine readers' responses to online discussion topics. |
| Outcome: | The proposed model outperforms the human model in predicting social emotions in a multilabel classification setting. |
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. |
Engage the Public: Poll Question Generation for Social Media Posts (2021.acl-long)
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| Challenge: | a novel application to generate poll questions for social media posts offers an easy way to hear the public's voice . for the silent majority, they tend to read others' messages instead of voicing their opinions with words . |
| Approach: | They propose to encode user comments and discover latent topics therein as contexts to generate poll questions for social media posts. |
| Outcome: | The proposed model outperforms popular models without exploiting topics from comments . human evaluations show it can generate high-quality polls useful to draw user engagements . |