What Gets Echoed? Understanding the "Pointers" in Explanations of Persuasive Arguments
This work addresses the challenge of improving AI-generated explanations by analyzing real-world data, though it is incremental in advancing methods for natural language processing tasks.
The authors tackled the problem of understanding how natural language explanations selectively reuse words from the arguments they explain, using a dataset of 36K explanations from a subreddit. They developed features that predict word echoing with relatively strong power and enhance neural explanation generation, revealing patterns like nouns being less likely echoed except for subjects and objects from specific sources.
Explanations are central to everyday life, and are a topic of growing interest in the AI community. To investigate the process of providing natural language explanations, we leverage the dynamics of the /r/ChangeMyView subreddit to build a dataset with 36K naturally occurring explanations of why an argument is persuasive. We propose a novel word-level prediction task to investigate how explanations selectively 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 neural methods of generating explanations. In particular, while the non-contextual properties of a word itself are more valuable for stopwords, the interaction between the constituent parts of an explanandum is crucial in predicting 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.