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Despite their success in a variety of NLP tasks, pre-trained language models,due to their heavy reliance on compositionality, fail in effectively capturingthe meanings of multiword expressions (MWEs), especially idioms. Therefore,datasets and methods to improve the representation of MWEs are urgently needed.Existing datasets are limited to providing the degree of idiomaticity ofexpressions along with the literal and, where applicable, (a single)non-literal interpretation of MWEs. This work presents a novel dataset ofnaturally occurring sentences containing MWEs manually classified into afine-grained set of meanings, spanning both English and Portuguese. We use thisdataset in two tasks designed to test i) a language model's ability to detectidiom usage, and ii) the effectiveness of a language model in generatingrepresentations of sentences containing idioms. Our experiments demonstratethat, on the task of detecting idiomatic usage, these models perform reasonablywell in the one-shot and few-shot scenarios, but that there is significantscope for improvement in the zero-shot scenario. On the task of representingidiomaticity, we find that pre-training is not always effective, whilefine-tuning could provide a sample efficient method of learning representationsof sentences containing MWEs.

AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models