Dialogue Corpus

Dialogue corpora are collections of conversational data used to train and evaluate dialogue systems, focusing on improving natural language understanding and generation. Current research emphasizes creating more nuanced corpora that capture the complexities of human conversation, including uncertainty in beliefs, blends of task-oriented and open-domain dialogue, and the expression and experience of emotions. This involves developing methods for unsupervised intent induction, improving dialogue state tracking through techniques like entity adaptive pre-training, and building more realistic user simulators using adversarial learning. These advancements are crucial for building more robust and human-like dialogue systems with applications in customer service, explainable AI, and other areas.