Dialogue Representation

Dialogue representation research focuses on creating effective computational models of conversations, aiming to capture the nuanced meaning and context within multi-turn interactions. Current efforts concentrate on improving pre-trained language models for dialogue understanding through techniques like contrastive learning, self-supervised learning, and the incorporation of commonsense knowledge, often employing transformer-based architectures. These advancements are crucial for improving various applications, including task-oriented dialogue systems, emotion recognition in conversations, and abstractive dialogue summarization, ultimately leading to more natural and human-like interactions with AI.