Prompt-based Pre-trained Model for Personality and Interpersonal Reactivity Prediction (2022)

Abstract: This paper describes the LingJing team’s method to 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 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. Extensive experiments are performed, which shows the effectiveness of the proposed method. On the final submission, our system achieves a Pearson Correlation Coefficient of 0.2301 and 0.2546 on Track 3 and Track 4 respectively. We ranked 1-st on both sub-tasks.

## introduction personality can be defined as a set of characteristics (e.g., age, income, and hobby), which can reflect the differences of individuals in thinking, emotions, and behaviours @xcite . the power of personality is worth exploring and pervades human lives everywhere @xcite . personality prediction is an interdisciplinary field spanning from psychology to computer science. however, people's personalities can't be directly observed and measured, but are expressed in activity patterns, and thus can be inferred in that way. humans tend to covey their personalities through language because it is the most prominent way in such an internet society. meanwhile, written text is one of the most important appearances of language. consequently, the involvement of machinelearning-based methods in predicting the personality of individuals seems necessary. over the past 20 years, much progress has been made in natural lan-guage processing (nlp), which is faced with a revolution. especially, with the development of deep learning and transfer learning, automatically and accurately predicting the personality is an emerging topic in nlp. classical text representation methods and pre-trained word representation approach both make the personality prediction research area more attractive and competitive. even though how to compute and predict someone's personality based on texts is still an open question, attracting more and more researchers to focus on it. the approaches of personality prediction have a long research history. early in 2008, the personae corpus @xcite has already been proposed for predicting the personality from the text. the corpus is used for predicting the writer's personality traits that are reflected in writing style. input representation is one of the most components in nlp. in recent years, the novel representation method has been the pretrained word embeddings. therefore, we mainly focus on the personality prediction approaches with the pre-trained word embeddings. methods with the pre-trained embeddings are firstly based on word2vec @xcite and glove @xcite . surprisingly, @xcite propose to extract common sense knowledge and affective information to recognize personality from text, where they represent information in a directed graph. despite the success of early word embedding models (e.g., word2vec), there are still practical problems. the first one is that previously unseen words make the model get into trouble. the second one is the overwhelmingly large parameters for a model to learn. @xcite propose a recurrent and compositional deep-learning-based model to address these issues. very recently, researchers start to explore large pre-trained models for nlp @xcite @xcite . @xcite use the bert language model @xcite to extract contextualized word embeddings and achieve state-of-the-art performance for personality detection. similarly, transformer-md @xcite is proposed to put multiple posts together for representing the personality of each user. the context embeddings learned by large pre-trained models can effectively improve the performances and have theoretical advantages over traditional embedding methods. therefore, we also adopt a pre-trained model named deberta @xcite to construct our personality prediction model. in this paper, we present our prompt-based pre-trained network for personality prediction at track 3 and track 4 of wassa-2022. track 3: personality prediction (per), which consists in predicting the personality of the essay writer, knowing all his/her essays and the news article from which they reacted. track 4: interpersonal reactivity index prediction (iri), which consists in predicting the personality of the essay writer, knowing all his/her essays and the news article from which they reacted. ## main method in this section, we will elaborate on our method in detail, including the model architecture, prompt design, regression optimization, data augmentation and model ensemble. ## experimental setting this section will subsequently present the emotion dataset, our experimental models, experimental settings, control of variables experiment. ## conclusion in this paper, we describe our system to the personality prediction and interpersonal reactivity index prediction sub-tasks. we used the deberta pre-trained language model as the backbone. the prompt is designed for providing the persona information to the pre-trained model. the data augmentation with random punctuation and model ensemble is adopted for better results. in the evaluation phase, our methods ranked top-1 on track 3 and track 4 respectively. in the future, we will focus on more effective prompt designing for performing the personality and interpersonal reactivity prediction.