Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize
This work addresses improving social interaction in conversational AI for users of voice assistants like Amazon Echo, but it is incremental as it builds on existing methods with heuristic personalization.
The paper tackled the problem of lacking rapport in open-domain conversational systems by developing a rule-based user model for personalization and testing personal opinion question strategies in the Alexa Prize competition. The results showed a statistically significant positive impact on perceived conversation quality and length.
There has been an increased focus on creating conversational open-domain dialogue systems in the spoken dialogue community. Unlike traditional dialogue systems, these conversational systems cannot assume any specific information need or domain restrictions, i.e., the only inherent goal is to converse with the user on an unknown set of topics. While massive improvements in Natural Language Understanding (NLU) and the growth of available knowledge resources can partially support a robust conversation, these conversations generally lack the rapport between two humans that know each other. We developed a robust open-domain conversational system, Athena, that real Amazon Echo users access and evaluate at scale in the context of the Alexa Prize competition. We experiment with methods intended to increase intimacy between Athena and the user by heuristically developing a rule-based user model that personalizes both the current and subsequent conversations and evaluating specific personal opinion question strategies in A/B studies. Our results show a statistically significant positive impact on perceived conversation quality and length when employing these strategies.