Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings
Table 2: The performance on FriendsPersona with 3 formats.
['Trait', 'Format', 'Majority', 'ABCNN', 'ABLSTM', 'HAN', 'BERT', 'RoBERTa']
First, the models perform the best on S out of 3 formats for 5 traits It makes sense because our models are originally designed to classify simple monologue text instead of multi-party dialogue text and S converts dialogue to the target speaker’s monologue. Second, BERT and RoBERTa together achieve the most best results (10 out of 15 cases). But BERT and RoBERTa do not beat other models for CON on both datasets. At last, HAN achieves 3 best results out of 15 cases on FriendsPersona, better than its performance (0 case) on Essays dataset. This is because HAN encodes dialogue on both utterance and token levels, which allows HAN to attend the main speaker’s utterances. In the future, we need a customized model to leverage dialogue information between speakers.