2022 article
Persona-Based Conversational AI: State of the Art and Challenges
2022 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS, ICDMW, pp. 993–1001.
TL;DR:
This study evaluates two strong baseline methods, the Ranking Profile Memory Network and the Poly-Encoder, on the NeurIPS ConvAI2 benchmark dataset and elucidates the importance of incorporating persona information into conversational systems.
(via Semantic Scholar)
open access via arxiv.org (repository)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Source: Web Of Science
Added: May 30, 2023
Conversational AI has become an increasingly prominent and practical application of machine learning. How-ever, existing conversational AI techniques still suffer from var-ious 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 meth-ods and outlines challenges and future research directions for advancing personalized conversational AI technology.