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Modelling Idiomatic Expressions in Abstract Meaning Representation

Idiomatic expressions, a subclass of multiword expressions (MWE), pose persistent challenges for semantic parsing, as their meanings often diverge from the compositional semantics of their constituent words and depend strongly on contextual cues. While Abstract Meaning Representation (AMR) parsers aim to capture sentence-level semantics in a structured graph form, existing datasets provide limited coverage of idiomatic language, constraining their ability to model such expressions accurately. To address this gap, we extended a subset of the MAGPIE dataset by constructing a corpus of potentially idiomatic expressions (PIE) annotated with their corresponding AMR graphs. The dataset includes both naturally occurring and synthetically generated sentences, covering idioms in literal and idiomatic contexts. We fine-tune a state-of-the-art AMR parser on this dataset and evaluate its capacity to generate context-sensitive graphs that correctly reflect idiomatic versus literal interpretations. Our results show that standard parsers often capture only literal meanings of such expressions, while fine-tuning on our dataset improves alignment with the intended interpretations.