Papers by Haijun Li
Zero-Shot Conversational Stance Detection: Dataset and Approaches (2025.findings-acl)
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| Challenge: | Existing stance detection datasets are limited to a limited set of specific targets . current models are limited in their ability to detect large numbers of unseen targets based on a large number of unidentified targets. |
| Approach: | They propose a speaker interaction and target-aware prototypical contrastive learning model that can detect public opinion towards specific targets using social media data. |
| Outcome: | The proposed model achieves state-of-the-art in zero-shot conversational stance detection with only an F1-macro score of 43.81%. |
Table-as-Search: Agentic Information Seeking is Table Completion (2026.findings-acl)
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Tian Lan, Felix Henry, Bin Zhu, Qianghuai Jia, Junyang Ren, Qihang PU, Haijun Li, Longyue Wang, Zhao Xu, Weihua Luo
| Challenge: | Current Information Seeking (InfoSeeking) agents struggle to maintain focus and coherence during long-horizon exploration, as tracking search states within one plain-text context is inherently fragile. |
| Approach: | They propose a structured planning framework that reformulates the InfoSeeking task as a Table Completion task. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks across three kinds of benchmarks, including multi-agent framework and commercial systems. |
Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication (2020.coling-main)
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| Challenge: | Existing methods for visual storytelling construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. |
| Approach: | They propose a topic description task to detect the global semantic context of an image stream and a story is then constructed with the guidance of the topic description. |
| Outcome: | The proposed framework can generate stories with higher quality compared to state-of-the-art methods on a VIST dataset. |