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Explanations are central to everyday life, and are a topic of growinginterest in the AI community. To investigate the process of providing naturallanguage explanations, we leverage the dynamics of the /r/ChangeMyViewsubreddit to build a dataset with 36K naturally occurring explanations of whyan argument is persuasive. We propose a novel word-level prediction task toinvestigate how explanations selectively reuse, or echo, information from whatis being explained (henceforth, explanandum). We develop features to capturethe properties of a word in the explanandum, and show that our proposedfeatures not only have relatively strong predictive power on the echoing of aword in an explanation, but also enhance neural methods of generatingexplanations. In particular, while the non-contextual properties of a worditself are more valuable for stopwords, the interaction between the constituentparts of an explanandum is crucial in predicting the echoing of content words.We also find intriguing patterns of a word being echoed. For example, althoughnouns are generally less likely to be echoed, subjects and objects can,depending on their source, be more likely to be echoed in the explanations.

What Gets Echoed? Understanding the "Pointers" in Explanations of Persuasive Arguments