“妈妈总是有一种解释事情的方式，所以我可以理解”：学习构建解释的对话语料库
"Mama Always Had a Way of Explaining Things So I Could Understand'': A Dialogue Corpus for Learning to Construct Explanations
摘要：
随着人工智能在日常生活中越来越普遍，人类越来越需要了解其行为和决策。大多数关于可解释人工智能的研究都建立在一个理想的解释的前提下。然而，事实上，日常解释是在解释者（解释者）和被解释者（被解释者）之间的对话中共同构建的。在本文中，我们介绍了第一个对话解释语料库，以使NLP研究人类如何解释以及人工智能如何学习模仿这个过程。语料库由65个来自Wired视频系列\emph{5 Levels}的英语对话组成，向5个不同水平的被解释者解释13个主题。所有1550个对话轮已经由五个独立的专业人员针对所讨论的主题以及针对对话行为和所执行的解释移动手动标记。我们分析了解释者和被解释者的语言模式，并探讨了不同熟练程度的差异。基于BERT的基线结果表明，序列信息有助于有效地预测主题，行为和动作
英文摘要：
As AI is more and more pervasive in everyday life, humans have an increasing demand to understand its behavior and decisions. Most research on explainable AI builds on the premise that there is one ideal explanation to be found. In fact, however, everyday explanations are co-constructed in a dialogue between the person explaining (the explainer) and the specific person being explained to (the explainee). In this paper, we introduce a first corpus of dialogical explanations to enable NLP research on how humans explain as well as on how AI can learn to imitate this process. The corpus consists of 65 transcribed English dialogues from the Wired video series \emph{5 Levels}, explaining 13 topics to five explainees of different proficiency. All 1550 dialogue turns have been manually labeled by five independent professionals for the topic discussed as well as for the dialogue act and the explanation move performed. We analyze linguistic patterns of explainers and explainees, and we explore differences across proficiency levels. BERT-based baseline results indicate that sequence information helps predicting topics, acts, and moves effectively
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
