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Showing 1 to 15 of 54 results Save | Export
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Frank, Stefan L. – Language Learning, 2021
Although computational models can simulate aspects of human sentence processing, research on this topic has remained almost exclusively limited to the single language case. The current review presents an overview of the state of the art in computational cognitive models of sentence processing, and discusses how recent sentence-processing models…
Descriptors: Multilingualism, Language Processing, Computational Linguistics, Psycholinguistics
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Li, Ping; Xu, Qihui – Language Learning, 2023
The last two decades have seen a significant amount of interest in bilingual language learning and processing. A number of computational models have also been developed to account for bilingualism, with varying degrees of success. In this article, we first briefly introduce the significance of computational approaches to bilingual language…
Descriptors: Bilingualism, Computational Linguistics, Second Language Learning, Second Language Instruction
Patience Stevens; David C. Plaut – Grantee Submission, 2022
The morphological structure of complex words impacts how they are processed during visual word recognition. This impact varies over the course of reading acquisition and for different languages and writing systems. Many theories of morphological processing rely on a decomposition mechanism, in which words are decomposed into explicit…
Descriptors: Written Language, Morphology (Languages), Word Recognition, Reading Processes
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Lieven, Elena; Ferry, Alissa; Theakston, Anna; Twomey, Katherine E. – First Language, 2020
During language acquisition children generalise at multiple layers of granularity. Ambridge argues that abstraction-based accounts suffer from lumping (over-general abstractions) or splitting (over-precise abstractions). Ambridge argues that the only way to overcome this conundrum is in a purely exemplar/analogy-based system in which…
Descriptors: Language Acquisition, Children, Generalization, Abstract Reasoning
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McClelland, James L. – First Language, 2020
Humans are sensitive to the properties of individual items, and exemplar models are useful for capturing this sensitivity. I am a proponent of an extension of exemplar-based architectures that I briefly describe. However, exemplar models are very shallow architectures in which it is necessary to stipulate a set of primitive elements that make up…
Descriptors: Models, Language Processing, Artificial Intelligence, Language Usage
Borgonovi, Francesca; Hervé, Justine; Seitz, Helke – OECD Publishing, 2023
The paper discusses the implications of recent advances in artificial intelligence for knowledge workers, focusing on possible complementarities and substitution between machine translation tools and language professionals. The emergence of machine translation tools could enhance social welfare through enhanced opportunities for inter-language…
Descriptors: Translation, Computational Linguistics, Accuracy, Second Languages
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Adger, David – First Language, 2020
The syntactic behaviour of human beings cannot be explained by analogical generalization on the basis of concrete exemplars: analogies in surface form are insufficient to account for human grammatical knowledge, because they fail to hold in situations where they should, and fail to extend in situations where they need to. [For Ben Ambridge's…
Descriptors: Syntax, Figurative Language, Models, Generalization
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Hartshorne, Joshua K. – First Language, 2020
Ambridge argues that the existence of exemplar models for individual phenomena (words, inflection rules, etc.) suggests the feasibility of a unified, exemplars-everywhere model that eschews abstraction. The argument would be strengthened by a description of such a model. However, none is provided. I show that any attempt to do so would immediately…
Descriptors: Models, Language Acquisition, Language Processing, Bayesian Statistics
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Chae-Eun Kim – Journal of Pan-Pacific Association of Applied Linguistics, 2022
This study explores how Korean-to-English machine translation (MT) systems (e.g., Google Translator, NAVER Papago) deal with Korean passive structures. Cross-linguistically, Korean and English passives show different ways to construct passive-voice sentences from active structure. English passives including with [to be + past participle] may have…
Descriptors: Korean, English (Second Language), Second Language Learning, Second Language Instruction
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Grüter, Theres – Second Language Research, 2021
In this commentary, I suggest that it may be helpful to think about the formidable problem space that Westergaard's (2021) Linguistic Proximity Model seeks to address at the three levels of analysis that Marr (1982) famously proposed are needed to understand any complex cognitive system. I argue that at the computational level of analysis, where…
Descriptors: Linguistic Theory, Second Language Learning, Multilingualism, Native Language
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Ambridge, Ben – First Language, 2020
In this response to commentators, I agree with those who suggested that the distinction between exemplar- and abstraction-based accounts is something of a false dichotomy and therefore move to an abstractions-made-of-exemplars account under which (a) we store all the exemplars that we hear (subject to attention, decay, interference, etc.) but (b)…
Descriptors: Language Acquisition, Syntax, Computational Linguistics, Language Research
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Vinall, Kimberly; Hellmich, Emily – L2 Journal, 2022
The world of language education is intimately and undeniably implicated in the presence, use, and development of machine translation software. On a classroom level, students are increasingly using machine translation in the classroom and in the "real world," through travel, study abroad, and work internships. On a professional level,…
Descriptors: Translation, Computational Linguistics, Second Languages, Language Processing
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Ehrensberger-Dow, Maureen; Delorme Benites, Alice; Lehr, Caroline – Interpreter and Translator Trainer, 2023
Recent developments in machine translation (MT) might have led some people to believe that soon professional translation will not be needed, but most translator trainers are aware of the high demand for the quality that MT systems cannot deliver without human intervention. It is thus important that professional translators, trainers and their…
Descriptors: Translation, Professional Education, Computational Linguistics, Computer Software
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Rose, Yvan – First Language, 2020
Ambridge's proposal cannot account for the most basic observations about phonological patterns in human languages. Outside of the earliest stages of phonological production by toddlers, the phonological systems of speakers/learners exhibit internal behaviours that point to the representation and processing of inter-related units ranging in size…
Descriptors: Phonology, Language Patterns, Toddlers, Language Processing
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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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