Prompt-based System for Personality and Interpersonal Reactivity Prediction

TL;DR

This paper introduces a prompt-based learning approach using pre-trained language models for predicting personality traits and interpersonal reactivity, incorporating data augmentation and ensemble methods to improve performance.

Contribution

It presents a novel prompt-based framework with personalized prompts and ensemble techniques for personality and reactivity prediction tasks.

Findings

Enhanced prediction accuracy through data augmentation.

Effective use of prompt design for personalized knowledge incorporation.

Open-source software implementation provided.

Abstract

This paper describes our proposed method for the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Personality Prediction (PER) and Reactivity Index Prediction (IRI). In this paper, we adopt the prompt-based learning method with the pre-trained language model to accomplish these tasks. Specifically, the prompt is designed to provide knowledge of the extra personalized information for enhancing the pre-trained model. Data augmentation and model ensemble are adopted for obtaining better results. Moreover, we also provided the online software demonstration and the codes of the software for further research.