  Previous works related to automatic personality recognition focus on using
traditional classification models with linguistic features. However, attentive
neural networks with contextual embeddings, which have achieved huge success in
text classification, are rarely explored for this task. In this project, we
have two major contributions. First, we create the first dialogue-based
personality dataset, FriendsPersona, by annotating 5 personality traits of
speakers from Friends TV Show through crowdsourcing. Second, we present a novel
approach to automatic personality recognition using pre-trained contextual
embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely
improve the state-of-art results on the monologue Essays dataset by 2.49%, and
establish a solid benchmark on our FriendsPersona. By comparing results in two
datasets, we demonstrate the challenges of modeling personality in multi-party
dialogue.
