Smart Topic Detection for Robot Conversation
Elise Russell, Richard J. Povinelli, Andrew B. Williams · Human-Robot Interaction · 2016
In order for humanoid robots to have believable conversations with humans, the robots will need a reliable method for detecting the topics shared in the interaction to formulate a relevant response. This paper presents a novel application of intelligent indexing and ontology analysis for use in conversational topic detection for human-robot interaction. We evaluate a method for training on a corpus of transcribed phone conversations and using a concept association matrix to determine the strongest common-sense linkages to words in the conversation. This model is placed within the conversation and emotion interface of our humanoid robot, MU-L8, and tested with users. Evaluation is performed both computationally, with the corpus, and perceptually with users, and the promising results are presented in this paper.