论文标题
对话人AI的互动教学
Interactive Teaching for Conversational AI
论文作者
论文摘要
当前的对话AI系统旨在了解一组预先设计的请求并执行相关的操作，从而限制它们自然发展并根据人类互动进行适应。本文以孩子们学习如何与成年人互动的方式进行动机，描述了一种新的可教学AI系统，该系统能够直接从最终用户使用实时互动教学课程学习新语言掘金，称为概念。拟议的设置使用三个模型来：a）识别在实时对话互动期间自动理解的差距，b）了解与用户实时互动的此类未知概念的解释，以及c）管理专门针对互动教学课程量身定制的课堂子教学。我们提出了基于最先进的变压器模型的神经体系结构，并在预训练的模型之上进行了微调，并在各个组件上显示出准确的改进。我们证明，这种方法在建立更具适应性和个性化的语言理解模型的指导下是非常有希望的。
Current conversational AI systems aim to understand a set of pre-designed requests and execute related actions, which limits them to evolve naturally and adapt based on human interactions. Motivated by how children learn their first language interacting with adults, this paper describes a new Teachable AI system that is capable of learning new language nuggets called concepts, directly from end users using live interactive teaching sessions. The proposed setup uses three models to: a) Identify gaps in understanding automatically during live conversational interactions, b) Learn the respective interpretations of such unknown concepts from live interactions with users, and c) Manage a classroom sub-dialogue specifically tailored for interactive teaching sessions. We propose state-of-the-art transformer based neural architectures of models, fine-tuned on top of pre-trained models, and show accuracy improvements on the respective components. We demonstrate that this method is very promising in leading way to build more adaptive and personalized language understanding models.
