Long Time No See! Open-Domain Conversation with Long-Term Persona Memory

• URL: http://arxiv.org/abs/2203.05797v2
• Date: Mon, 14 Mar 2022 12:01:20 GMT
• Title: Long Time No See! Open-Domain Conversation with Long-Term Persona Memory
• Authors: Xinchao Xu, Zhibin Gou, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Haifeng Wang, Shihang Wang
• Abstract summary: We present a novel task of Long-term Memory Conversation (LeMon) We then build a new dialogue dataset DuLeMon and a dialogue generation framework with Long-Term Memory (LTM) mechanism. Results on DuLeMon indicate that PLATO-LTM can significantly outperform baselines in terms of long-term dialogue consistency.
• Score: 37.51131984324123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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