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

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 2911–2921, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 2911 What Gets Echoed? Understanding the “Pointers” in Explanations of Persuasive Arguments David Atkinson and Kumar Bhargav Srinivasan and Chenhao Tan Department of Computer Science University of Colorado Boulder Boulder, CO david.i.atkinson, kumar.srinivasan, chenhao.tan@colorado.edu Abstract Explanations are central to everyday life, and are a topic of growing interest in the AI com- munity. To investigate the process of providing natural language explanations, we leverage the dynamics of the /r/ChangeMyView subred- dit to build a dataset with 36K naturally occur- ring explanations of why an argument is per- suasive. We propose a novel word-level pre- diction task to investigate how explanations se- lectively reuse, or echo, information from what is being explained (henceforth, explanandum). We develop features to capture the properties of a word in the explanandum, and show that our proposed features not only have relatively strong predictive power on the echoing of a word in an explanation, but also enhance neu- ral methods of generating explanations. In particular, while the non-contextual properties of a word itself are more valuable for stop- words, the interaction between the constituent parts of an explanandum is crucial in predict- ing the echoing of content words. We also find intriguing patterns of a word being echoed. For example, although nouns are generally less likely to be echoed, subjects and objects can, depending on their source, be more likely to be echoed in the explanations. 1 Introduction Explanations are essential for understanding and learning (Keil, 2006). They can take many forms, ranging from everyday explanations for questions such as why one likes Star Wars, to sophisti- cated formalization in the philosophy of science (Salmon, 2006), to simply highlighting features in recent work on interpretable machine learning (Ribeiro et al., 2016). Although everyday explanations are mostly en- coded in natural language, natural language expla- nations remain understudied in NLP, partly due to a lack of appropriate datasets and problem formulations. To address these challenges, we leverage /r/ChangeMyView, a community dedi- cated to sharing counterarguments to controver- sial views on Reddit, to build a sizable dataset of naturally-occurring explanations. Specifically, in /r/ChangeMyView, an original poster (OP) first delineates the rationales for a (controver- sial) opinion (e.g., in Table 1, “most hit music artists today are bad musicians”). Members of /r/ChangeMyView are invited to provide counter- arguments. If a counterargument changes the OP’s view, the OP awards a ∆ to indicate the change and is required to explain why the counterargu- ment is persuasive. In this work, we refer to what is being explained, including both the orig- inal post and the persuasive comment, as the ex- planandum.1 An important advantage of explanations in /r/ChangeMyView is that the explanandum con- tains most of the required information to provide its explanation. These explanations often select key counterarguments in the persuasive comment and connect them …

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