Papers by Zhixin Bai
PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (2026.tacl-1)
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Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, Ruifeng Xu
| Challenge: | Experimental results demonstrate that a Pruned interpretable knowledge Graph Learning framework for explainable stance detection is state-of-the-art for social media stance prediction. |
| Approach: | They propose a Pruned interpretable knowledge Graph Learning framework for explainable stance detection that incorporates commonsense knowledge and prunes redundant information to ensure precision and minimize noise. |
| Outcome: | The proposed framework achieves state-of-the-art on three public datasets. |
Is Compound Aspect-Based Sentiment Analysis Addressed by LLMs? (2024.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis (ABSA) aims to predict aspect-based elements from text . large language models (LLMs) have impressive abilities in handling human instructions . |
| Approach: | They propose a framework to evaluate LLMs' ability to handle complex ABSA tasks . they use constrained prompts to automatically organize the returned predictions . |
| Outcome: | The proposed framework outperforms supervised methods in some cases, but it is still lacking in other areas. |
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect sentiment quad prediction aims to predict aspects due to distinct data distribution. |
| Approach: | They propose a method that aggregates multiple templates with a broader view . they first construct a few-shot ASQP dataset that contains richer categories . |
| Outcome: | The proposed method outperforms the state-of-the-art methods under four few-shot settings and other public datasets. |