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Most of the open-domain dialogue models tend to perform poorly in the settingof long-term human-bot conversations. The possible reason is that they lack thecapability of understanding and memorizing long-term dialogue historyinformation. To address this issue, we present a novel task of Long-term MemoryConversation (LeMon) and then build a new dialogue dataset DuLeMon and adialogue generation framework with Long-Term Memory (LTM) mechanism (calledPLATO-LTM). This LTM mechanism enables our system to accurately extract andcontinuously update long-term persona memory without requiring multiple-sessiondialogue datasets for model training. To our knowledge, this is the firstattempt to conduct real-time dynamic management of persona information of bothparties, including the user and the bot. Results on DuLeMon indicate thatPLATO-LTM can significantly outperform baselines in terms of long-term dialogueconsistency, leading to better dialogue engagingness.

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