- Ming Sun, Yun-Nung (Vivian) Chen, Alexander I. Rudnicky. 2015. Understanding User ’ s Cross-Domain Intentions in Spoken Dialog Systems. Abstract: Spoken language based intelligent assistants (IAs) have been developed for a number of domains but their functionality has mostly been confined to the scope of a given app. One reason is that it’s is difficult for IAs to infer a user’s intent without access to relevant context and unless explicitly implemented, context is not available across app boundaries. We describe context-aware multi-app dialog systems that can learn to 1) identify meaningful user intents; 2) produce natural language representation for the semantics of such intents; and 3) predict user intent as they engage in multi-app tasks. As part of our work we collected data from the smartphones of 14 users engaged in real-life multi-app tasks. We found that it is reasonable to group tasks into high-level intentions. Based on the dialog content, IA can generate useful phrases to describe the intention. We also found that, with readily available contexts, IAs can effectively predict user’s intents during conversation, with accuracy at 58.9%.
