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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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Schuler, Kathryn D.; Kodner, Jordan; Caplan, Spencer – First Language, 2020
In 'Against Stored Abstractions,' Ambridge uses neural and computational evidence to make his case against abstract representations. He argues that storing only exemplars is more parsimonious -- why bother with abstraction when exemplar models with on-the-fly calculation can do everything abstracting models can and more -- and implies that his…
Descriptors: Language Processing, Language Acquisition, Computational Linguistics, Linguistic Theory
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Brown, Kevin – CEA Forum, 2015
In this article, the author describes his project to take every standardized exam English majors students take. During the summer and fall semesters of 2012, the author signed up for and took the GRE General Test, the Praxis Content Area Exam (English Language, Literature, and Composition: Content Knowledge), the Senior Major Field Tests in…
Descriptors: College Faculty, College English, Test Preparation, Standardized Tests