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
Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues
Sandhya Singh, Prapti Roy, Nihar Sahoo, Niteesh Mallela, Himanshu Gupta, Pushpak Bhattacharyya, Milind Savagaonkar, Nidhi, Roshni Ramnani, Anutosh Maitra, Shubhashis Sengupta
A Unified Framework for Emotion Identification and Generation in Dialogues
Avinash Madasu, Mauajama Firdaus, Asif Eqbal
Predicting Corporate Risk by Jointly Modeling Company Networks and Dialogues in Earnings Conference Calls
Yunxin Sang, Yang Bao
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
Prakhar Gupta, Cathy Jiao, Yi-Ting Yeh, Shikib Mehri, Maxine Eskenazi, Jeffrey P. Bigham