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

Code

• github.com/MorningBooks/A_oveview_of_awesome_causality_dataset
  none★ 20
• github.com/MorningBooks/Causality
  none★ 0

Abstract

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.

Tasks

Reproductions