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We propose a novel method for selecting coherent and diverse responses for agiven dialogue context. The proposed method re-ranks response candidatesgenerated from conversational models by using event causality relations betweenevents in a dialogue history and response candidates (e.g., ``be stressed out''precedes ``relieve stress''). We use distributed event representation based onthe Role Factored Tensor Model for a robust matching of event causalityrelations due to limited event causality knowledge of the system. Experimentalresults showed that the proposed method improved coherency and dialoguecontinuity of system responses.

Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding