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Analogy-making gives rise to reasoning, abstraction, flexible categorizationand counterfactual inference -- abilities lacking in even the best AI systemstoday. Much research has suggested that analogies are key to non-brittlesystems that can adapt to new domains. Despite their importance, analogiesreceived little attention in the NLP community, with most research focusing onsimple word analogies. Work that tackled more complex analogies relied heavilyon manually constructed, hard-to-scale input representations. In this work, weexplore a more realistic, challenging setup: our input is a pair of naturallanguage procedural texts, describing a situation or a process (e.g., how theheart works/how a pump works). Our goal is to automatically extract entitiesand their relations from the text and find a mapping between the differentdomains based on relational similarity (e.g., blood is mapped to water). Wedevelop an interpretable, scalable algorithm and demonstrate that it identifiesthe correct mappings 87% of the time for procedural texts and 94% for storiesfrom cognitive-psychology literature. We show it can extract analogies from alarge dataset of procedural texts, achieving 79% precision (analogy prevalencein data: 3%). Lastly, we demonstrate that our algorithm is robust toparaphrasing the input texts.

Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes