Dialogue Utterance
Dialogue utterance research focuses on understanding and modeling the complexities of conversational exchanges, aiming to improve human-computer interaction and AI capabilities. Current research emphasizes developing models that accurately capture nuances like personality, emotion, and uncertainty in dialogue, often leveraging large language models (LLMs) and contrastive learning techniques for improved performance. This work is significant for advancing AI's ability to engage in natural, contextually aware conversations, with applications ranging from improved chatbots and virtual assistants to more effective tools for healthcare and education.
Papers
Unraveling ChatGPT: A Critical Analysis of AI-Generated Goal-Oriented Dialogues and Annotations
Tiziano Labruna, Sofia Brenna, Andrea Zaninello, Bernardo Magnini
Multi-Granularity Prompts for Topic Shift Detection in Dialogue
Jiangyi Lin, Yaxin Fan, Xiaomin Chu, Peifeng Li, Qiaoming Zhu
Reducing Sensitivity on Speaker Names for Text Generation from Dialogues
Qi Jia, Haifeng Tang, Kenny Q. Zhu
MultiModal-GPT: A Vision and Language Model for Dialogue with Humans
Tao Gong, Chengqi Lyu, Shilong Zhang, Yudong Wang, Miao Zheng, Qian Zhao, Kuikun Liu, Wenwei Zhang, Ping Luo, Kai Chen
ARDIE: AR, Dialogue, and Eye Gaze Policies for Human-Robot Collaboration
Chelsea Zou, Kishan Chandan, Yan Ding, Shiqi Zhang