Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding
Shohei Tanaka, Koichiro Yoshino, Katsuhito Sudoh, Satoshi Nakamura · 2019
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.