Visible to the public Hierarchical Module Classification in Mixed-Initiative Conversational Agent System

TitleHierarchical Module Classification in Mixed-Initiative Conversational Agent System
Publication TypeConference Paper
Year of Publication2017
AuthorsSuzanna, Sia Xin Yun, Anthony, Li Lianjie
Conference NameProceedings of the 2017 ACM on Conference on Information and Knowledge Management
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4918-5
Keywordsconversational agent, conversational agents, dialogue systems, Human Behavior, language modeling, machine learning, Metrics, pubcrawl, Scalability

Our operational context is a task-oriented dialog system where no single module satisfactorily addresses the range of conversational queries from humans. Such systems must be equipped with a range of technologies to address semantic, factual, task-oriented, open domain conversations using rule-based, semantic-web, traditional machine learning and deep learning. This raises two key challenges. First, the modules need to be managed and selected appropriately. Second, the complexity of troubleshooting on such systems is high. We address these challenges with a mixed-initiative model that controls conversational logic through hierarchical classification. We also developed an interface to increase interpretability for operators and to aggregate module performance.

Citation Keysuzanna_hierarchical_2017