Automatic Evaluation of Neural Personality-based Chatbots

Abstract

Stylistic variation is critical to render the utterances generated by conversational agents natural and engaging. In this paper, we focus on sequence-to-sequence models for open-domain dialogue response generation and propose a new method to evaluate the extent to which such models are able to generate responses that reflect different personality traits.

To appear in the Proceedings of the 11th International Conference on Natural Language Generation (INLG-2018)