论文标题
长时间看不到！与长期角色记忆的开放域对话
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory
论文作者
论文摘要
在长期人物对话的情况下，大多数开放域对话模型往往表现不佳。可能的原因是他们缺乏理解和记住长期对话历史信息的能力。为了解决这个问题，我们介绍了长期记忆对话（柠檬）的新任务，然后构建一个新的对话数据集Dulemon和带有长期内存（LTM）机制（称为plato-ltm）的对话生成框架。该LTM机制使我们的系统能够准确提取并连续更新长期的角色记忆，而无需进行多个课程对话数据集以进行模型培训。据我们所知，这是对包括用户和机器人在内的双方角色信息进行实时动态管理的首次尝试。 Dulemon的结果表明，柏拉图-LTM在长期对话一致性方面可以大大优于基线，从而提高对话的吸引力。
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.
