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Previous works related to automatic personality recognition focus on usingtraditional classification models with linguistic features. However, attentiveneural networks with contextual embeddings, which have achieved huge success intext classification, are rarely explored for this task. In this project, wehave two major contributions. First, we create the first dialogue-basedpersonality dataset, FriendsPersona, by annotating 5 personality traits ofspeakers from Friends TV Show through crowdsourcing. Second, we present a novelapproach to automatic personality recognition using pre-trained contextualembeddings (BERT and RoBERTa) and attentive neural networks. Our models largelyimprove the state-of-art results on the monologue Essays dataset by 2.49%, andestablish a solid benchmark on our FriendsPersona. By comparing results in twodatasets, we demonstrate the challenges of modeling personality in multi-partydialogue.

Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings