This paper presents a novel teachable conversation interaction system that is capable of learning users preferences from cold start by gradually adapting to personal preferences. In particular, the TAI system is able to automatically identify and label user preference in live interactions, manage dialogue flows for interactive teaching sessions, and reuse learned preference for preference elicitation. We develop the TAI system by leveraging BERT encoder models to encode both dialogue and relevant context information, and build action prediction (AP), argument filling (AF) and named entity recognition (NER) models to understand the teaching session. We adopt a seeker-provider interaction loop mechanism to generate diverse dialogues from cold-start. TAI is capable of learning user preference, which achieves 0.9122 turn level accuracy on out-of-sample dataset, and has been successfully adopted in production.

翻译：本文提出了一种新颖的可教学对话交互系统，该系统能够从冷启动状态逐步适应用户个性化偏好，进而学习用户偏好。具体而言，TAI系统能够在实时交互中自动识别并标注用户偏好，管理交互式教学会话的对话流程，并复用已习得的偏好进行偏好引导。我们通过利用BERT编码器模型对对话及相关上下文信息进行编码，并构建动作预测、参数填充及命名实体识别模型来理解教学会话。我们采用寻求者-提供者交互循环机制，从冷启动状态生成多样化对话。TAI能够学习用户偏好，在样本外数据集上实现了0.9122的轮次级准确率，并已成功应用于生产环境中。