Year: 2022
Type: conference-paper
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 PLATO-LTM with a Long-Term Memory (LTM) mechanism.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 1 .
Authors: Xinchao Xu, Zhibin Gou, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Haifeng Wang, Shihang Wang
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4226484906