Enhancing Impression Change Prediction in Speed Dating Simulations Based on Speakers' Personalities

TL;DR

This paper introduces a method for improving impression change prediction in speed dating dialogue simulations by incorporating speakers' personalities, leading to more realistic and positively received interactions.

Contribution

It proposes a novel approach that considers both utterance impact and speaker personalities to enhance dialogue simulation accuracy.

Findings

Personality-aware models better predict impression improvements.

Incorporating personalities improves human-rated dialogue quality.

The method outperforms previous approaches that ignore personality factors.

Abstract

This paper focuses on simulating text dialogues in which impressions between speakers improve during speed dating. This simulation involves selecting an utterance from multiple candidates generated by a text generation model that replicates a specific speaker's utterances, aiming to improve the impression of the speaker. Accurately selecting an utterance that improves the impression is crucial for the simulation. We believe that whether an utterance improves a dialogue partner's impression of the speaker may depend on the personalities of both parties. However, recent methods for utterance selection do not consider the impression per utterance or the personalities. To address this, we propose a method that predicts whether an utterance improves a partner's impression of the speaker, considering the personalities. The evaluation results showed that personalities are useful in predicting impression changes per utterance. Furthermore, we conducted a human evaluation of simulated dialogues using our method. The results showed that it could simulate dialogues more favorably received than those selected without considering personalities.