# Modeling Dyadic Conversations for Personality Inference

arXiv: https://arxiv.org/abs/2009.12496 · HTML: https://mlsanity.com/paper/2009.12496/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [PersonaTAB: Predicting Personality Traits using Textual, Acoustic, and Behavioral Cues in Fully-Duplex Speech Dialogs](https://mlsanity.com/paper/2505.14356/) (2025)

## Abstract (arXiv)

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.