Modeling the Quality of Dialogical Explanations

TL;DR

This paper investigates the factors that contribute to successful explanation dialogues by analyzing Reddit conversations, comparing them to expert dialogues, and using language models to predict explanation quality.

Contribution

It introduces a novel corpus of 399 Reddit explanation dialogues, analyzes interaction flows, and demonstrates improved prediction of explanation success using language models.

Findings

Interaction flows correlate with explanation success

Encoding interaction flows improves prediction accuracy

Reddit dialogues share features with expert explanations

Abstract

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.