Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings
Table 1: The performance of models in accuracy on Essays.
['[BOLD] Models', '[BOLD] AGR', '[BOLD] CON', '[BOLD] EXT', '[BOLD] OPN', '[BOLD] NEU']
First, we experiment baseline models on Essays dataset with FastText embeddings. ABCNN and ABLSTM represents CNN and Bidirectional LSTM models with attention mechanism. HAN is Hierarchical Attention Network. Besides, we fine-tuned pre-trained base BERT and RoBERTa embeddings for the task. Overall, ABCNN achieves the best score on CON, whereas RoBERTa gets the best on the other four traits. We improve 2.49% for 5 traits on average (AGR by 2.22%, CON by 2.83%, EXT by 2.53%, OPN by 3.18%, NEU by 1.69%).