Surprisal Dynamics for the Detection of Multi-Word Expressions in English

AbstractThis work examines the potential of surprisal slope as a feature for identifying multi-word expressions (MWEs) in English, leveraging token-level surprisal estimates from the GPT-2 language model. Evaluations on the DiMSUM and SemEval-2022 datasets reveal that surprisal slope provides moderate yet meaningful discriminative power with a trade-off between specificity and coverage: while high recall indicates that surprisal slope captures many true MWEs, the slightly lower precision reflects false positives, particularly for non-MWEs that follow formulaic patterns (e.g., adjective-noun or verb-pronoun structures). The method performs particularly well for conventionalized expressions, such as idiomatic bigrams in the SemEval-2022 corpus. Both idiomatic and literal usages of these bigrams exhibit negative slopes, with idiomatic instances generally showing a more pronounced decrease.Overall, surprisal slope offers a cognitively motivated and interpretable signal that complements existing MWE identification methods, particularly for conventionalized expressions.