A Long Hard Look at MWEs in the Age of Language Models

In recent years, language models (LMs) have become almost synonymous with NLP. Pre-trained to “read” a large text corpus, such models are useful as both a representation layer as well as a source of world knowledge. But how well do they represent MWEs? This talk will discuss various problems in representing MWEs, and the extent to which LMs address them: • Do LMs capture the implicit relationship between constituents in compositional MWEs (from baby oil through parsley cake to cheeseburger stabbing)? • Do LMs recognize when words are used nonliterally in non-compositional MWEs (e.g. do they know whether there are fleas in the flea market)? • Do LMs know idioms, and can they infer the meaning of new idioms from the context as humans often do?