Dialogue Modeling

Dialogue modeling in artificial intelligence focuses on creating systems that can engage in natural and meaningful conversations, encompassing both spoken and written interactions. Current research emphasizes improving model robustness by addressing limitations such as handling simultaneous speech, multi-party conversations, and the incorporation of non-verbal cues like gestures. This involves developing new datasets, leveraging techniques like data augmentation and pseudo-stereo data generation, and refining model architectures, often based on transformers, to better capture context and shared understanding (common ground). Advances in this field are crucial for enhancing human-computer interaction and creating more sophisticated conversational agents for various applications.