论文标题
“妈妈总是有一种解释事物的方法
"Mama Always Had a Way of Explaining Things So I Could Understand'': A Dialogue Corpus for Learning to Construct Explanations
论文作者
论文摘要
随着人工智能在日常生活中越来越普遍，人类对了解其行为和决策的需求越来越大。关于可解释的AI的大多数研究基于一个理想的解释。但是，实际上，每天的解释是在解释（解释者）与要解释的特定人员（解释）之间的对话中共同构建的。在本文中，我们介绍了对话解释的第一个语料库，以使NLP研究人类如何解释以及AI如何学会模仿这一过程。该语料库由有线视频系列\ emph {5级}中的65个转录的英语对话组成，向五个不同熟练程度的说明者解释了13个主题。所有1550个对话转弯均由五名独立专业人员手动标记为讨论的主题以及对话法和进行解释动作。我们分析了解释器和解释者的语言模式，并探讨了跨熟练程度的差异。基于BERT的基线结果表明，序列信息有助于预测主题，行动和有效移动
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
