论文标题
部分可观测时空混沌系统的无模型预测
Persona-Based Conversational AI: State of the Art and Challenges
论文作者
论文摘要
会话AI已成为机器学习的越来越重要且实用的应用。但是，现有的对话人工智能技术仍有各种局限性。这样的局限性之一是缺乏结合辅助信息的发达方法，这些方法可以帮助模型更好地理解对话环境。在本文中，我们探讨了基于角色的信息如何帮助提高对话中响应的质量。首先，我们提供了一份文献综述，重点是利用角色信息的当前最新方法。我们在Neurips Conver2基准数据集上评估了两种强大的基线方法，即排名轮廓内存网络和多型编码器。我们的分析阐明了将角色信息纳入会话系统的重要性。此外，我们的研究通过当前的最新方法和概述了推进个性化对话AI技术的挑战和未来的研究方向，强调了一些局限性。
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.
