EmoPerso：通过自监督情感感知建模增强人格检测
EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling
AI总结：
针对文本人格检测依赖标注数据、忽略情感与人格交互的问题，提出自监督框架EmoPerso，通过生成式数据增强、情感-人格多任务联合优化、交叉注意力捕捉细粒度交互及自学习迭代增强推理，在两个基准数据集上超越现有最优模型。
摘要：
从文本中检测人格通常通过分析用户的社交媒体帖子来实现。然而，现有方法严重依赖大规模标注数据集，这使得获取高质量人格标签颇具挑战。此外，大多数研究将情感与人格视为独立变量，忽略了二者之间的相互作用。本文提出了一种新颖的自监督框架EmoPerso，该框架通过情感感知建模提升人格检测效果。EmoPerso首先利用生成机制进行合成数据增强与丰富的表征学习，随后提取伪标签情感特征，并通过多任务学习将其与人格预测联合优化；框架还采用交叉注意力模块捕捉人格特质与推断出的情感表征之间的细粒度交互。为进一步优化关系推理，EmoPerso采用自学习策略迭代增强模型的推理能力。在两个基准数据集上开展的大量实验表明，EmoPerso的性能超越了当前最优模型。源代码可在https://github.com/slz0925/EmoPerso获取。
英文摘要：
Personality detection from text is commonly performed by analysing users' social media posts. However, existing methods heavily rely on large-scale annotated datasets, making it challenging to obtain high-quality personality labels. Moreover, most studies treat emotion and personality as independent variables, overlooking their interactions. In this paper, we propose a novel self-supervised framework, EmoPerso, which improves personality detection through emotion-aware modelling. EmoPerso first leverages generative mechanisms for synthetic data augmentation and rich representation learning. It then extracts pseudo-labeled emotion features and jointly optimizes them with personality prediction via multi-task learning. A cross-attention module is employed to capture fine-grained interactions between personality traits and the inferred emotional representations. To further refine relational reasoning, EmoPerso adopts a self-taught strategy to enhance the model's reasoning capabilities iteratively. Extensive experiments on two benchmark datasets demonstrate that EmoPerso surpasses state-of-the-art models. The source code is available at https://github.com/slz0925/EmoPerso.
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。
