Affective-NLI：迈向对话中准确且可解释的人格识别
Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation
AI总结：
针对对话人格识别忽视情绪与人格语义的问题，提出Affective-NLI，结合对话情绪识别微调与NLI蕴含框架，提升准确性和可解释性，并显著优于现有方法。
摘要：
对话中的人格识别（PRC）旨在通过文本对话内容识别说话者的人格特质。这对于在人机交互（HCI）的各类应用中提供个性化服务至关重要，例如基于AI的心理治疗和老年人陪伴机器人。最近的大多数研究都分析对话内容以进行人格分类，却忽视了两个阻碍其性能的主要问题。首先，对话中包含的关键隐性因素被忽略，例如能够反映说话者人格的情绪。其次，只关注输入的对话内容而忽视对人格本身的语义理解，这会降低结果的可解释性。本文提出了情感自然语言推理（Affective-NLI），用于准确且可解释的PRC。为利用对话内容中的情感性以实现准确的人格识别，我们专门针对对话中的情绪识别微调了一个预训练语言模型，从而为话语提供实时情感标注。为提升识别结果的可解释性，我们通过判断对话内容是否蕴含人格标签的文本描述，将人格识别构建为一个NLI问题。在两个日常对话数据集上进行的大量实验表明，Affective-NLI显著优于最先进方法（提升6%-7%）。此外，我们的Flow实验表明，Affective-NLI能够在对话早期阶段准确识别说话者的人格，以22%-34%的优势超越最先进方法。
英文摘要：
Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content. It is essential for providing personalized services in various applications of Human-Computer Interaction (HCI), such as AI-based mental therapy and companion robots for the elderly. Most recent studies analyze the dialog content for personality classification yet overlook two major concerns that hinder their performance. First, crucial implicit factors contained in conversation, such as emotions that reflect the speakers' personalities are ignored. Second, only focusing on the input dialog content disregards the semantic understanding of personality itself, which reduces the interpretability of the results. In this paper, we propose Affective Natural Language Inference (Affective-NLI) for accurate and interpretable PRC. To utilize affectivity within dialog content for accurate personality recognition, we fine-tuned a pre-trained language model specifically for emotion recognition in conversations, facilitating real-time affective annotations for utterances. For interpretability of recognition results, we formulate personality recognition as an NLI problem by determining whether the textual description of personality labels is entailed by the dialog content. Extensive experiments on two daily conversation datasets suggest that Affective-NLI significantly outperforms (by 6%-7%) state-of-the-art approaches. Additionally, our Flow experiment demonstrates that Affective-NLI can accurately recognize the speaker's personality in the early stages of conversations by surpassing state-of-the-art methods with 22%-34%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
