MIDAS: A Dialog Act Annotation Scheme for Open Domain Human Machine Spoken Conversations

Abstract

Dialog act prediction is an essential language comprehension task for both dialog system building and discourse analysis. Previous dialog act schemes, such as SWBD-DAMSL, are designed for human-human conversations, in which conversation partners have perfect language understanding ability. In this paper, we design a dialog act annotation scheme, MIDAS (Machine Interaction Dialog Act Scheme), targeted on open-domain human-machine conversations. MIDAS is designed to assist machines which have limited ability to understand their human partners. MIDAS has a hierarchical structure and supports multi-label annotations. We collected and annotated a large open-domain human-machine spoken conversation dataset (consists of 24K utterances). To show the applicability of the scheme, we leverage transfer learning methods to train a multi-label dialog act prediction model and reach an F1 score of 0.79.

Cited by in corpus (9)

• User Interaction Patterns and Breakdowns in Conversing with LLM-Powered Voice Assistants
• Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations
• Gunrock 2.0: A User Adaptive Social Conversational System
• Gunrock: A Social Bot for Complex and Engaging Long Conversations
• Understanding Learner-LLM Chatbot Interactions and the Impact of Prompting Guidelines
• Athena: Constructing Dialogues Dynamically with Discourse Constraints
• Intentonomy: a Dataset and Study towards Human Intent Understanding
• Few-shot Intent Classification and Slot Filling with Retrieved Examples
• A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments