Papers by Dazhen Wan
S^4: Operationalizing Speech Act Theory for Strategic Semi-Structured Psychiatric Interview (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for psychiatric interviewing degenerate into rigid interrogation or aimless chitchat due to a lack of strategic planning. |
| Approach: | They propose a framework for psychiatric interviewing grounded in Speech Act Theory that integrates a large-scale dataset with fine-grained psychic speech act annotations. |
| Outcome: | The proposed framework outperforms baselines in psychiatric interviewing. |
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format (2023.emnlp-demo)
Copied to clipboard
Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang
| Challenge: | Existing tools for building TOD systems often lack a user-friendly interface . a toolkit with advanced, easily integrable modules is needed to bridge this gap . |
| Approach: | They propose a multifaceted dialogue system toolkit that integrates diverse datasets and models with a streamlined training process and in-depth evaluation tools. |
| Outcome: | The proposed toolkit combines RL and transfer learning to support the rapid development and evaluation of robust dialogue policies. |
Robustness Testing of Language Understanding in Task-Oriented Dialog (2021.acl-long)
Copied to clipboard
Jiexi Liu, Ryuichi Takanobu, Jiaxin Wen, Dazhen Wan, Hongguang Li, Weiran Nie, Cheng Li, Wei Peng, Minlie Huang
| Challenge: | a lack of systematic studies on the robustness of language understanding models in task-oriented dialog systems is limiting . authors propose a model-agnostic toolkit LAUG to approximate natural language perturbations . |
| Approach: | They propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness of language understanding models in task-oriented dialog systems. |
| Outcome: | The proposed toolkit reveals critical robustness issues in state-of-the-art models. |
CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)
Copied to clipboard
Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions. |
| Approach: | They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges. |
| Outcome: | Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4. |
PsychePass: Calibrating LLM Therapeutic Competence via Trajectory-Anchored Tournaments (2026.findings-acl)
Copied to clipboard
| Challenge: | evaluating therapeutic competence of large language models remains challenging due to unstructured and longitudinal nature of counseling. |
| Approach: | They propose a framework that calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments. |
| Outcome: | The proposed framework calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments. |
A Unified Dialogue User Simulator for Few-shot Data Augmentation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to augment large-scale task-oriented dialogues rely on annotated data. |
| Approach: | They propose to build a unified dialogue user simulation model by pre-training on publicly available datasets. |
| Outcome: | The proposed model can be tuned on a target domain with few-shot data. |