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Noun compound interpretation is the task of expressing a noun compound (e.g.chocolate bunny) in a free-text paraphrase that makes the relationship betweenthe constituent nouns explicit (e.g. bunny-shaped chocolate). We proposemodifications to the data and evaluation setup of the standard task (Hendrickxet al., 2013), and show that GPT-3 solves it almost perfectly. We theninvestigate the task of noun compound conceptualization, i.e. paraphrasing anovel or rare noun compound. E.g., chocolate crocodile is a crocodile-shapedchocolate. This task requires creativity, commonsense, and the ability togeneralize knowledge about similar concepts. While GPT-3's performance is notperfect, it is better than that of humans -- likely thanks to its access tovast amounts of knowledge, and because conceptual processing is effortful forpeople (Connell and Lynott, 2012). Finally, we estimate the extent to whichGPT-3 is reasoning about the world vs. parroting its training data. We findthat the outputs from GPT-3 often have significant overlap with a large webcorpus, but that the parroting strategy is less beneficial for novel nouncompounds.

From chocolate bunny to chocolate crocodile: Do Language Models Understand Noun Compounds?