Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an {\em explainer} discusses a concept or phenomenon of interest with an {\em explainee}. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee's side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum {\em Explain Like I am Five} and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.

翻译：解释在我们的生活中无处不在。它们通常以对话形式出现，其中一位“解释者”与一位“被解释者”讨论某个概念或感兴趣的现象。由于两者之间存在知识差距，让被解释者获得清晰的理解并非易事。以往的研究着眼于成功对话中解释策略、对话行为与话题在专家解释者引导下的互动。然而，日常生活中的解释常常失败，这引发了一个问题：是什么使对话成功？在本研究中，我们从解释者与被解释者的互动角度出发，探讨这些互动如何与解释质量（即被解释者成功理解的程度）相关联。具体而言，我们首先构建了一个包含399个对话的语料库，这些对话来自Reddit论坛的“像对五岁小孩解释”（Explain Like I am Five）板块，并对互动流程和解释质量进行了标注。接着，我们分析了互动流程，并将其与专家对话中的互动流程进行了比较。最后，我们使用两种能处理长文本输入的语言模型对互动流程进行编码，并提供了实证证据，表明该编码在预测解释对话成功与否方面能显著提升效果。