@article{maudslay-teufel-2026-simulating,
title = "Simulating Sense Extension",
author = "Maudslay, Rowan Hall and
Teufel, Simone",
journal = "Transactions of the Association for Computational Linguistics",
volume = "14",
year = "2026",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2026.tacl-1.82/",
doi = "10.1162/tacl.a.780",
pages = "1826--1850",
abstract = "Humans have an uncanny ability to push words beyond their limits. This ability manifests in many phenomena, including metaphor, metonymy, semantic drift, slang, jargon, conversion, and overextension. Generative models of these phenomena are uncommon, because there is no robust methodology that can be used to train and evaluate models of this nature. To address this, we introduce a new task, novel sense formulation, in which a model is exposed to a multimodal representation of an unseen concept and must use an existing word creatively to describe it. We create seven datasets corresponding to the phenomena above, and evaluate a perceptron, a transformer, and an influential cognitive model. For most phenomena, a multimodal variant of the perceptron performed best. The cognitive model underperformed, suggesting that its description of the mechanism behind sense extension is incomplete."
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<abstract>Humans have an uncanny ability to push words beyond their limits. This ability manifests in many phenomena, including metaphor, metonymy, semantic drift, slang, jargon, conversion, and overextension. Generative models of these phenomena are uncommon, because there is no robust methodology that can be used to train and evaluate models of this nature. To address this, we introduce a new task, novel sense formulation, in which a model is exposed to a multimodal representation of an unseen concept and must use an existing word creatively to describe it. We create seven datasets corresponding to the phenomena above, and evaluate a perceptron, a transformer, and an influential cognitive model. For most phenomena, a multimodal variant of the perceptron performed best. The cognitive model underperformed, suggesting that its description of the mechanism behind sense extension is incomplete.</abstract>
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%0 Journal Article
%T Simulating Sense Extension
%A Maudslay, Rowan Hall
%A Teufel, Simone
%J Transactions of the Association for Computational Linguistics
%D 2026
%V 14
%I MIT Press
%C Cambridge, MA
%F maudslay-teufel-2026-simulating
%X Humans have an uncanny ability to push words beyond their limits. This ability manifests in many phenomena, including metaphor, metonymy, semantic drift, slang, jargon, conversion, and overextension. Generative models of these phenomena are uncommon, because there is no robust methodology that can be used to train and evaluate models of this nature. To address this, we introduce a new task, novel sense formulation, in which a model is exposed to a multimodal representation of an unseen concept and must use an existing word creatively to describe it. We create seven datasets corresponding to the phenomena above, and evaluate a perceptron, a transformer, and an influential cognitive model. For most phenomena, a multimodal variant of the perceptron performed best. The cognitive model underperformed, suggesting that its description of the mechanism behind sense extension is incomplete.
%R 10.1162/tacl.a.780
%U https://aclanthology.org/2026.tacl-1.82/
%U https://doi.org/10.1162/tacl.a.780
%P 1826-1850
Markdown (Informal)
[Simulating Sense Extension](https://aclanthology.org/2026.tacl-1.82/) (Maudslay & Teufel, TACL 2026)
ACL
- Rowan Hall Maudslay and Simone Teufel. 2026. Simulating Sense Extension. Transactions of the Association for Computational Linguistics, 14:1826–1850.