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

TL;DR

This paper reviews current persona-based conversational AI methods, evaluates two baseline models on a benchmark dataset, and discusses challenges and future directions for improving personalized response generation.

Contribution

It provides a comprehensive review, empirical evaluation of baseline models, and insights into limitations and future research challenges in persona-based conversational AI.

Findings

Persona information improves response relevance.

Baseline models show varying effectiveness with persona data.

Current methods face challenges in personalization and context understanding.

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.