Work on personality detection has tended to incorporate psychological features from different personality models, such as BigFive and MBTI. There are more than 900 psychological features, each of which is helpful for personality detection. However, when used in combination, the application of different calculation standards among these features may result in interference between features calculated using distinct systems, thereby introducing noise and reducing performance. This paper adapts different psychological models in the proposed PsyAttention for personality detection, which can effectively encode psychological features, reducing their number by 85%. In experiments on the BigFive and MBTI models, PysAttention achieved average accuracy of 65.66% and 86.30%, respectively, outperforming state-of-the-art methods, indicating that it is effective at encoding psychological features.

翻译：关于人格检测的研究通常倾向于整合来自不同人格模型（如大五人格和MBTI）的心理特征。目前存在900余种心理特征，每种特征都对人格检测具有辅助价值。然而，当综合运用这些特征时，其计算标准间的差异性可能导致不同计算框架下的特征相互干扰，从而引入噪声并降低模型性能。本文提出的PsyAttention通过适配不同心理模型实现人格检测，该模型能够有效编码心理特征，且将特征维度缩减85%。基于大五人格和MBTI模型的实验表明，PysAttention分别取得65.66%和86.30%的平均准确率，优于现有最优方法，证明其编码心理特征的有效性。