ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents

Understand

Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers' intents, not bound to a particular dialogue system.

• Unfortunately, publicly available data sets with DA annotation are all based on different annotation schemes and thus incompatible with each other.
• Moreover, their schemes often do not cover all aspects necessary for open-domain human-machine interaction.
• In this paper, we propose a methodology to map several publicly available corpora to a subset of the ISO standard, in order to create a large task-independent training corpus for DA classification.