通过情绪感知建模提升文本人格识别效果
EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling
- 自监督框架生成合成数据并联合优化情绪与人格预测
- 跨注意力机制捕捉人格与情绪的细粒度交互关系
- 适合需要低标注成本人格分析的研究者使用
从文本中进行人格检测通常依赖社交媒体用户的发言，但现有方法严重依赖大规模标注数据，获取高质量人格标签困难。此外，多数研究将情绪与人格视为独立变量，忽视二者交互。本文提出新颖的自监督框架 EmoPerso，通过情绪感知建模提升人格检测性能。EmoPerso 首先利用生成机制进行合成数据增强和丰富表征学习，随后提取伪标签情绪特征，并通过多任务学习联合优化情绪与人格预测。采用跨注意力模块捕捉人格特质与推断情绪表征之间的细粒度交互。为进一步强化关系推理能力，EmoPerso 采用自教策略迭代优化模型。在两个基准数据集上的大量实验表明，EmoPerso 超越现有最佳模型。源代码已公开于 https://github.com/slz0925/EmoPerso。
原文摘要 · Abstract (English)
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.
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