Automatic Evaluation of Neural Personality-based Chatbots
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Sep 2018.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 3; 2024: 1.
6 papers.
- 2023RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generationcited by 25
- 2023A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbotscited by 5
- 2024PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Healthcited by 5
- 2021Persona Authentication through Generative Dialoguecited by 4
- 2021WeaSuL: Weakly Supervised Dialogue Policy Learning: Reward Estimation for Multi-turn Dialoguecited by 2
- 2023Hi Sheldon! Creating Deep Personalized Characters from TV Showscited by 0
- 2024PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Health22 Dec 2024
- 2023A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots31 Dec 2023
- 2023RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation12 Jun 2023
- 2023Hi Sheldon! Creating Deep Personalized Characters from TV Shows9 Apr 2023
- 2021Persona Authentication through Generative Dialogue25 Oct 2021
- 2021WeaSuL: Weakly Supervised Dialogue Policy Learning: Reward Estimation for Multi-turn Dialogue1 Aug 2021