Long Time No See! Open-Domain Conversation with Long-Term Persona Memory
Xinchao Xu, Zhibin Gou, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Haifeng Wang, Shihang Wang
Abstract
Most of the open-domain dialogue models tend to perform poorly in the setting of long-term human-bot conversations. The possible reason is that they lack the capability of understanding and memorizing long-term dialogue history information. To address this issue, we present a novel task of Long-term Memory Conversation (LeMon) and then build a new dialogue dataset DuLeMon and a dialogue generation framework with Long-Term Memory (LTM) mechanism (called PLATO-LTM). This LTM mechanism enables our system to accurately extract and continuously update long-term persona memory without requiring multiple-session dialogue datasets for model training. To our knowledge, this is the first attempt to conduct real-time dynamic management of persona information of both parties, including the user and the bot. Results on DuLeMon indicate that PLATO-LTM can significantly outperform baselines in terms of long-term dialogue consistency, leading to better dialogue engagingness.
BibTeX
@inproceedings{xu-etal-2022-long,
title = "Long Time No See! Open-Domain Conversation with Long-Term Persona Memory",
author = "Xu, Xinchao and
Gou, Zhibin and
Wu, Wenquan and
Niu, Zheng-Yu and
Wu, Hua and
Wang, Haifeng and
Wang, Shihang",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.207/",
doi = "10.18653/v1/2022.findings-acl.207",
pages = "2639--2650"
}