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As AI is more and more pervasive in everyday life, humans have an increasingdemand to understand its behavior and decisions. Most research on explainableAI builds on the premise that there is one ideal explanation to be found. Infact, however, everyday explanations are co-constructed in a dialogue betweenthe person explaining (the explainer) and the specific person being explainedto (the explainee). In this paper, we introduce a first corpus of dialogicalexplanations to enable NLP research on how humans explain as well as on how AIcan learn to imitate this process. The corpus consists of 65 transcribedEnglish dialogues from the Wired video series \emph{5 Levels}, explaining 13topics to five explainees of different proficiency. All 1550 dialogue turnshave been manually labeled by five independent professionals for the topicdiscussed as well as for the dialogue act and the explanation move performed.We analyze linguistic patterns of explainers and explainees, and we exploredifferences across proficiency levels. BERT-based baseline results indicatethat sequence information helps predicting topics, acts, and moves effectively

"Mama Always Had a Way of Explaining Things So I Could Understand'': A Dialogue Corpus for Learning to Construct Explanations