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
Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models
Anthony Sicilia, Hyunwoo Kim, Khyathi Raghavi Chandu, Malihe Alikhani, Jack Hessel
With a Little Help from my (Linguistic) Friends: Topic Segmentation of Multi-party Casual Conversations
Amandine Decker, Maxime Amblard
Plan-Grounded Large Language Models for Dual Goal Conversational Settings
Diogo Glória-Silva, Rafael Ferreira, Diogo Tavares, David Semedo, João Magalhães
HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent
Weijie Xu, Zicheng Huang, Wenxiang Hu, Xi Fang, Rajesh Kumar Cherukuri, Naumaan Nayyar, Lorenzo Malandri, Srinivasan H. Sengamedu