CASPR: A Commonsense Reasoning-based Conversational Socialbot
This work addresses the challenge of building conversational AI systems that can truly understand users, but it appears incremental as it builds on existing socialbot frameworks with specific reasoning enhancements.
The authors tackled the problem of creating a socialbot that can understand and engage in human-like conversation by developing CASPR, which uses automated commonsense reasoning and conversational knowledge templates, and reported on its performance in the Amazon Alexa Socialbot Challenge 4.
We report on the design and development of the CASPR system, a socialbot designed to compete in the Amazon Alexa Socialbot Challenge 4. CASPR's distinguishing characteristic is that it will use automated commonsense reasoning to truly "understand" dialogs, allowing it to converse like a human. Three main requirements of a socialbot are that it should be able to "understand" users' utterances, possess a strategy for holding a conversation, and be able to learn new knowledge. We developed techniques such as conversational knowledge template (CKT) to approximate commonsense reasoning needed to hold a conversation on specific topics. We present the philosophy behind CASPR's design as well as details of its implementation. We also report on CASPR's performance as well as discuss lessons learned.