Papers by Jiawang Hao

2 papers
Enhancing Emotional Support Conversations: A Framework for Dynamic Knowledge Filtering and Persona Extraction (2025.coling-main)

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Challenge: Existing dialogue models struggle to interpret context accurately due to irrelevant or misclassified knowledge, limiting their effectiveness in real-world scenarios.
Approach: They propose a framework that dynamically filters relevant commonsense knowledge and extracts personalized information to improve empathetic dialogue generation.
Outcome: The proposed framework outperforms existing models in coherence, emotional understanding, and response relevance on the ESConv dataset.
Beyond Coarse Labels: Fine-Grained Problem Augmentation and Multi-Dimensional Feedback for Emotional Support Conversation (2025.findings-emnlp)

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Challenge: Existing ESC datasets often use coarse-grained problem categories, limiting models’ ability to address users’ complex, overlapping challenges.
Approach: They propose a generalizable fine-grained problem enhancement method that augments problem types, user scenarios, and profiles, enabling the construction of richer and more diverse ESC corpora.
Outcome: The proposed method improves both automatic and human evaluation metrics across different models.

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