Persona-Based Conversational AI: State of the Art and Challenges

• URL: http://arxiv.org/abs/2212.03699v1
• Date: Sun, 4 Dec 2022 18:16:57 GMT
• Title: Persona-Based Conversational AI: State of the Art and Challenges
• Authors: Junfeng Liu, Christopher Symons, Ranga Raju Vatsavai
• Abstract summary: We explore how persona-based information could help improve the quality of response generation in conversations. Our study highlights several limitations with current state-of-the-art methods and outlines challenges and future research directions for advancing personalized conversational AI technology.
• Score: 5.7817077975444136
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Conversational AI has become an increasingly prominent and practical application of machine learning. However, existing conversational AI techniques still suffer from various limitations. One such limitation is a lack of well-developed methods for incorporating auxiliary information that could help a model understand conversational context better. In this paper, we explore how persona-based information could help improve the quality of response generation in conversations. First, we provide a literature review focusing on the current state-of-the-art methods that utilize persona information. We evaluate two strong baseline methods, the Ranking Profile Memory Network and the Poly-Encoder, on the NeurIPS ConvAI2 benchmark dataset. Our analysis elucidates the importance of incorporating persona information into conversational systems. Additionally, our study highlights several limitations with current state-of-the-art methods and outlines challenges and future research directions for advancing personalized conversational AI technology.

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