A Summarized History-based Dialogue System for Amnesia-Free Prompt Updates
Abstract
In today's society, information overload presents challenges in providing optimal recommendations. Consequently, the importance of dialogue systems that can discern and provide the necessary information through dialogue is increasingly recognized. However, some concerns existing dialogue systems rely on pre-trained models and need help to cope with real-time or insufficient information. To address these concerns, models that allow the addition of missing information to dialogue robots are being proposed. Yet, maintaining the integrity of previous conversation history while integrating new data remains a formidable challenge. This paper presents a novel system for dialogue robots designed to remember user-specific characteristics by retaining past conversation history even as new information is added.
Keywords
Cite
@article{arxiv.2312.13891,
title = {A Summarized History-based Dialogue System for Amnesia-Free Prompt Updates},
author = {Hyejin Hong and Hibiki Kawano and Takuto Maekawa and Naoki Yoshimaru and Takamasa Iio and Kenji Hatano},
journal= {arXiv preprint arXiv:2312.13891},
year = {2023}
}
Comments
This paper is part of the proceedings of the Dialogue Robot Competition 2023