ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents
This work addresses the lack of standardized training data for dialogue act tagging, which is crucial for improving conversational agents, though it is incremental as it builds on existing corpora and standards.
The authors tackled the problem of incompatible dialogue act annotation schemes by mapping multiple corpora to an ISO standard subset, creating a large domain-independent training corpus. They demonstrated the feasibility of training a domain-independent tagger on this corpus, achieving robustness across different dialogue act categories.
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. We show the feasibility of using this corpus to train a domain-independent DA tagger testing it on out-of-domain conversational data, and argue the importance of training on multiple corpora to achieve robustness across different DA categories.