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