Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detection

TL;DR

This paper introduces MvP, a multi-view mixture-of-experts model that automatically analyzes user-generated content from multiple perspectives to improve textual personality detection, employing regularization and multi-task learning for better performance.

Contribution

It proposes a novel multi-view mixture-of-experts framework with user consistency regularization and multi-task training for enhanced personality detection from text.

Findings

Outperforms existing methods on two datasets.

Effectively integrates multi-perspective analysis.

Improves personality detection accuracy.

Abstract

Textual personality detection aims to identify personality traits by analyzing user-generated content. To achieve this effectively, it is essential to thoroughly examine user-generated content from various perspectives. However, previous studies have struggled with automatically extracting and effectively integrating information from multiple perspectives, thereby limiting their performance on personality detection. To address these challenges, we propose the Multi-view Mixture-of-Experts Model for Textual Personality Detection (MvP). MvP introduces a Multi-view Mixture-of-Experts (MoE) network to automatically analyze user posts from various perspectives. Additionally, it employs User Consistency Regularization to mitigate conflicts among different perspectives and learn a multi-view generic user representation. The model's training is optimized via a multi-task joint learning strategy that balances supervised personality detection with self-supervised user consistency constraints. Experimental results on two widely-used personality detection datasets demonstrate the effectiveness of the MvP model and the benefits of automatically analyzing user posts from diverse perspectives for textual personality detection.