论文标题
迁移AI：迁移上下文的个性化对话对话
Migratable AI: Personalizing Dialog Conversations with migration context
论文作者
论文摘要
最近探索了对话式AI代理在不同实施方案中迁移到不同实施方案，以维持任务的连续性，以进一步改善用户体验。但是，在与用户的对话对话时，这些可迁移的代理缺乏对用户信息和迁移设备的上下文理解。这就打开了一个问题，即代理商在迁移到实施方案中如何行事，以预测下一个话语。我们从众包工人之间的对话对话中收集了一个数据集，其迁移上下文涉及在不同的环境（公共或私人）实施例中涉及个人和非个人话语，代理商迁移。我们使用和不迁移上下文培训了数据集上的生成和信息检索模型，并报告了定性指标和人类评估的结果。我们认为，迁移数据集将有助于培训未来的迁移AI系统。
The migration of conversational AI agents across different embodiments in order to maintain the continuity of the task has been recently explored to further improve user experience. However, these migratable agents lack contextual understanding of the user information and the migrated device during the dialog conversations with the user. This opens the question of how an agent might behave when migrated into an embodiment for contextually predicting the next utterance. We collected a dataset from the dialog conversations between crowdsourced workers with the migration context involving personal and non-personal utterances in different settings (public or private) of embodiment into which the agent migrated. We trained the generative and information retrieval models on the dataset using with and without migration context and report the results of both qualitative metrics and human evaluation. We believe that the migration dataset would be useful for training future migratable AI systems.
