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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)