Automatic Personality Recognition via XLNet with Refined Highway and Switching Module for Chatbot
Oscal T.‐C. Chen, Cheng-Hong Tsai, Manh-Hung Ha · 2024
This study introduces an Automatic Personality Recognition (APR) model, named XLNet refined-highway-switch network, comprising XLNet, refined highway units, a switching module, and a fully-connected layer. The pretrained XLNet is employed to extract semantic features from text. The refined highway units with the dense connections are explored. Additionally, our study delves into the optimized computation structures between the refined highway and switching module to unearth personality- related features. Notably, this APR model incorporates punctuation and stop words in text, resulting in a notable accuracy boost of up to 6.79%. Through extensive experiments on the integrated datasets which combine My Personality, Essays, and Friends Persona, the proposed model achieves the best average accuracies of64.79% and 64.43% for Big-5 personality traits on English and Chinese versions, respectively, as compared to conventional models. Furthermore, we implemented the proposed APR model in a retrieval-based chatbot, utilizing Jaccard distance to select the most suitable personality responses for interactions, yielding promising results in subject tests. Consequently, the proposed APR model can be widely used in various personalized applications related to personality traits.