Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning
Abstract:Idioms can be analysed in terms of their decomposability, the extent to which constituent meanings contribute to the figurative whole. Decomposability is thought to predict syntactic flexibility. Usage-based accounts instead attribute idiom behaviour to distributional experience, such as speaker familiarity and predictability. We examine these views using contextualised language models as controlled distributional learners. We propose a model-internal measure of decomposability and relate it to human ratings, syntactic flexibility, and predictability while tracking idiom learning during pretraining. Model-derived decomposability correlates weakly with human judgments and shows a small but consistent negative relationship with syntactic flexibility. Pretraining analyses show that stabilisation of idiom representations in models is not explained by frequency alone. Instead, surprisal, decomposability, and frequency all contribute, with decomposability showing the strongest training-dependent effect.
| Comments: | ACL 2026 Main - long paper (9 pages + Appendices) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.03817 [cs.CL] |
| (or arXiv:2606.03817v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.03817
arXiv-issued DOI via DataCite (pending registration)
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Hallucination Is Linearly Decodable from Mid-Layer Hidden States in Quantized LLMs
Jun 3
-
Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation
Jun 3
-
IdiomX A Multilingual Benchmark for Idiom Understanding, Retrieval, and Interpretation
Jun 3
-
Greener Than Humans? Environmental Attitudes in Large Language Models
Jun 3
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.