Papers by Hanlei Zhang
LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition (2025.emnlp-main)
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| Challenge: | Existing methods for understanding intents from multimodal signals exhibit limitations in their modality-level reliance, constraining relational reasoning over fine-grained semantics for complex intent understanding. |
| Approach: | They propose a method that harnesses the expansive knowledge of large language models to establish semantic foundations that boost smaller models’ relational reasoning performance. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multimodal intent and dialogue act recognition tasks and shows consistent performance gains across diverse semantic understanding scenarios. |
Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances (2024.acl-long)
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| Challenge: | Existing methods for semantics discovery focus on text, video, and audio, failing to leverage the rich multimodal information in the real world. |
| Approach: | They propose a method to construct augmentation views for multimodal data and use them to perform pre-training to establish well-initialized representations for subsequent clustering. |
| Outcome: | The proposed method improves on benchmark multimodal intent and dialogue act datasets by 2-6% over state-of-the-art methods. |
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition (2021.acl-demo)
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| Challenge: | TEXTOIR is the first integrated platform for text open intent recognition . currently, many dialogue systems are limited to handle the uncertain open intents . |
| Approach: | TEXTOIR is the first integrated platform for text open intent recognition . it is composed of two main modules: open intent detection and open intent discovery . authors propose a framework to implement a complete process to identify known intents and discover open intents . |
| Outcome: | TEXTOIR is the first integrated and visualized platform for text open intent recognition . it integrates state-of-the-art algorithms and benchmark intent datasets . however, there are still some issues, which bring difficulties for future research . |