论文标题
有效表示解释器与用户之间的协作行为
Effective Representation to Capture Collaboration Behaviors between Explainer and User
论文作者
论文摘要
可解释的AI（XAI）模型旨在为其所做的预测或行动提供透明度（以理由，解释等的形式）。最近，人们非常关注建立XAI模型，尤其是为了理解和解释深度学习模型的预测提供解释。在UCLA，我们提出了一个通用框架，以自然语言与XAI模型进行交互。
An explainable AI (XAI) model aims to provide transparency (in the form of justification, explanation, etc) for its predictions or actions made by it. Recently, there has been a lot of focus on building XAI models, especially to provide explanations for understanding and interpreting the predictions made by deep learning models. At UCLA, we propose a generic framework to interact with an XAI model in natural language.
