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Mahowald, Kyle; Kachergis, George; Frank, Michael C. – First Language, 2020
Ambridge calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec -- which have been extremely successful in modern natural language processing (NLP) applications -- count? Although these models often have ample parametric complexity to store exemplars from their training data, they also…
Descriptors: Models, Language Processing, Computational Linguistics, Language Acquisition
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Knabe, Melina L.; Vlach, Haley A. – First Language, 2020
Ambridge argues that there is widespread agreement among child language researchers that learners store linguistic abstractions. In this commentary the authors first argue that this assumption is incorrect; anti-representationalist/exemplar views are pervasive in theories of child language. Next, the authors outline what has been learned from this…
Descriptors: Child Language, Children, Language Acquisition, Models
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Ambridge, Ben – First Language, 2020
The goal of this article is to make the case for a radical exemplar account of child language acquisition, under which unwitnessed forms are produced and comprehended by on-the-fly analogy across multiple stored exemplars, weighted by their degree of similarity to the target with regard to the task at hand. Across the domains of (1) word meanings,…
Descriptors: Language Acquisition, Morphology (Languages), Phonetics, Phonology
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Tonini, Elisabetta; Lecce, Serena; Del Sette, Paola; Bianco, Federica; Canal, Paolo; Bambini, Valentina – First Language, 2022
Although metaphors are essential tools in everyday communication and educational settings, the literature lacks evidence of effective training tools to promote metaphor comprehension in typical development. Grounding in theoretical pragmatics, we developed a novel metaphor comprehension training (MetaCom) for school-age children that focuses on…
Descriptors: Figurative Language, Language Processing, Transfer of Training, Reading Comprehension