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Human conversations consist of reasonable and natural topic flows, which areobserved as the shifts of the mentioned concepts across utterances. Previouschatbots that incorporate the external commonsense knowledge graph prove thatmodeling the concept shifts can effectively alleviate the dull anduninformative response dilemma. However, there still exists a gap between theconcept relations in the natural conversation and those in the externalcommonsense knowledge graph, which is an issue to solve. Specifically, theconcept relations in the external commonsense knowledge graph are notintuitively built from the conversational scenario but the world knowledge,which makes them insufficient for the chatbot construction. To bridge the abovegap, we propose the method to supply more concept relations extracted from theconversational corpora and reconstruct an enhanced concept graph for thechatbot construction. In addition, we present a novel, powerful, and fast graphencoding architecture named the Edge-Transformer to replace the traditional GNNarchitecture. Experimental results on the Reddit conversation dataset indicateour proposed method significantly outperforms strong baseline systems andachieves new SOTA results. Further analysis individually proves theeffectiveness of the enhanced concept graph and the Edge-Transformerarchitecture.

Guiding Topic Flows in the Generative Chatbot by Enhancing the ConceptNet with the Conversation Corpora