Modeling Dyadic Conversations for Personality Inference

TL;DR

This paper introduces a novel augmented GRU model that leverages dyadic conversations to improve the accuracy of personality inference, outperforming traditional content-based methods.

Contribution

It proposes an augmented GRU model for learning unsupervised personal conversational embeddings from dyadic conversations, enhancing personality inference accuracy.

Findings

Modeling dyadic conversations significantly improves personality inference.

The proposed method outperforms traditional content-based approaches.

Experimental results confirm the effectiveness of the augmented GRU model.

Abstract

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.