Dialogue Act

Dialogue act (DA) classification focuses on automatically identifying the communicative intent behind utterances in conversations, aiming to improve the naturalness and effectiveness of human-computer interaction. Current research emphasizes developing robust and efficient DA classifiers, often employing multi-task learning, hierarchical models, and large language models (LLMs) to leverage contextual information and handle diverse dialogue structures, including those in multi-party and task-oriented settings. These advancements are crucial for building more sophisticated dialogue systems across various applications, from educational tools and virtual assistants to mental health support and automated evaluation of conversational AI. The field is actively addressing challenges such as data imbalance, low-resource scenarios, and computational efficiency in deploying these models.