ZOTBOT: using reading comprehension and commonsense reasoning in conversational agents
We describe the ZOTBOT system for open-ended conversations, designed for the Alexa Prize competition. We focus on two main shortcomings in existing conversational agents: lack of awareness in commonsense reasoning when responding to user utterances (resulting in nonsensical or uninteresting responses) and inability to understand semantics and converse naturally about fact-based articles in a compelling manner. First, we combine existing work in commonsense KBs, pretrained language models, and graph completion models to generate natural and intuitive responses, consistent with our commonsense knowledge, for open-domain utterances. Second, we utilize question generation models for both reading com- prehension and conversational followups for discussing fact-based articles. These contributions are implemented within a system engineered to be modular, allowing easy injection of manually-scripted responses, as well as supporting a detailed logging and analysis system. We present examples and analyses highlighting the benefits and shortcomings of ZOTBOT, and conclude with the lessons learned for future research.