Papers by Beibei Zhang
Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection (2024.lrec-main)
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| Challenge: | Few-shot Event Detection (FSED) requires limited labeled data and expensive manual labeling. |
| Approach: | They propose a prototype-based prompt-instance Interaction with causal Intervention model to utilize both prompts and verbalizers and effectively eliminate all biases. |
| Outcome: | The proposed model utilizes both prompts and verbalizers and eliminates all biases on RAMS and ACE datasets. |
Coarse-to-Fine Multimodal Information Selection for Video Speaking Style Recognition with Large Language Models (2026.findings-acl)
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| Challenge: | Video speaking style recognition (VSSR) aims to classify conversations into different types . integrating all multimodal data yields suboptimal results, authors say . |
| Approach: | They propose a framework that allows users to obtain multimodal data via coarse-to-fine selection . they propose to use visual captions and textual dialogues to integrate multimodal information . |
| Outcome: | The proposed framework outperforms existing training-free approaches and most training-based methods on multiple datasets. |
DLM: A Decoupled Learning Model for Long-tailed Polyphone Disambiguation in Mandarin (2024.naacl-long)
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| Challenge: | Grapheme-to-phoneme conversion datasets suffer from the long-tail problem . context learning for polyphonic characters often stems from a single dimension . |
| Approach: | They propose a model for long-tailed polyphone disambiguation in Mandarin that decouples representation and classification learnings. |
| Outcome: | The proposed model can decouple representation and classification learnings . it achieves transition learning of context from local to global . |