  Nowadays, automatical personality inference is drawing extensive attention
from both academia and industry. Conventional methods are mainly based on user
generated contents, e.g., profiles, likes, and texts of an individual, on
social media, which are actually not very reliable. In contrast, dyadic
conversations between individuals can not only capture how one expresses
oneself, but also reflect how one reacts to different situations. Rich
contextual information in dyadic conversation can explain an individual's
response during his or her conversation. In this paper, we propose a novel
augmented Gated Recurrent Unit (GRU) model for learning unsupervised Personal
Conversational Embeddings (PCE) based on dyadic conversations between
individuals. We adjust the formulation of each layer of a conventional GRU with
sequence to sequence learning and personal information of both sides of the
conversation. Based on the learned PCE, we can infer the personality of each
individual. We conduct experiments on the Movie Script dataset, which is
collected from conversations between characters in movie scripts. We find that
modeling dyadic conversations between individuals can significantly improve
personality inference accuracy. Experimental results illustrate the successful
performance of our proposed method.
