Talking Topically to Artificial Dialog Partners: Emulating Humanlike Topic Awareness in a Virtual Agent

During dialog, humans are able to track ongoing topics, to detect topical shifts, to refer to topics via labels, and to decide on the appropriateness of potential dialog topics. As a result, they interactionally produce coherent sequences of spoken utterances assigning a thematic structure to the whole conversation. Accordingly, an artificial agent that is intended to engage in natural and sophisticated human-agent dialogs should be endowed with similar conversational abilities. This paper presents how to enable topically coherent conversations between humans and interactive systems by emulating humanlike topic awareness in the virtual agent Max. Therefore, we firstly realized automatic topic detection and tracking on the basis of contextual knowledge provided by Wikipedia and secondly adapted the agent’s conversational behavior by means of the gained topic information. As a result, we contribute to improve human-agent dialogs by enabling topical talk between human and artificial interlocutors. This paper is a revised and extended version of [1].