Multilingual Age of Exposure 2.0
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| Title: | Multilingual Age of Exposure 2.0 |
|---|---|
| Language: | English |
| Authors: | Robert-Mihai Botarleanu (ORCID |
| Source: | International Journal of Artificial Intelligence in Education. 2024 34(4):1353-1377. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 25 |
| Publication Date: | 2024 |
| Sponsoring Agency: | Institute of Education Sciences (ED) Office of Naval Research (ONR) (DOD) |
| Contract Number: | R305A180261 R305A190050 N000142012623 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Multilingualism, English (Second Language), Second Language Learning, Second Language Instruction, Age Differences, Semantics, Word Lists, Vocabulary Development, Artificial Intelligence, Scores, Simulation, Networks, Prediction, Models, Computational Linguistics, Natural Language Processing, Spanish, French, German |
| DOI: | 10.1007/s40593-023-00386-7 |
| ISSN: | 1560-4292 1560-4306 |
| Abstract: | Age of Acquisition (AoA) scores approximate the age at which a language speaker fully understands a word's semantic meaning and represent a quantitative measure of the relative difficulty of words in a language. AoA word lists exist across various languages, with English having the most complete lists that capture the largest percentage of the vocabulary. In contrast, other languages have smaller lists making large-scale analyses difficult. Given the usefulness of AoA scores, methods have been developed to leverage the use of Machine Learning models to estimate AoA scores automatically through Age of Exposure (AoE) scores for the entire vocabulary of a language. These generated AoE scores use simulated learning trajectories to evaluate properties similar to AoA. In this work, we propose a method that leverages the greater size of existing English AoA lists to improve the performance of AoE prediction models for other languages. Our main contributions are threefold. First, we introduce a novel AoE regression architecture that uses a Recurrent Neural Network applied to the simulated word exposure trajectories. Second, we consider word embeddings projected into a unified multilingual space. Third, we apply transfer learning on the English AoE regressor to improve the performance of non-English AoE regressors. We show that AoA lists across languages share inherent similarities that enable Machine Learning models to transfer insights from one language to another, thus diminishing the effect of the smaller sample sizes for non-English languages. |
| Abstractor: | As Provided |
| Notes: | https://github.com/readerbench/Age-of-Exposure/tree/master/resources |
| IES Funded: | Yes |
| Entry Date: | 2024 |
| Accession Number: | EJ1453674 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: EJ1453674 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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AoA word lists exist across various languages, with English having the most complete lists that capture the largest percentage of the vocabulary. In contrast, other languages have smaller lists making large-scale analyses difficult. Given the usefulness of AoA scores, methods have been developed to leverage the use of Machine Learning models to estimate AoA scores automatically through Age of Exposure (AoE) scores for the entire vocabulary of a language. These generated AoE scores use simulated learning trajectories to evaluate properties similar to AoA. In this work, we propose a method that leverages the greater size of existing English AoA lists to improve the performance of AoE prediction models for other languages. Our main contributions are threefold. First, we introduce a novel AoE regression architecture that uses a Recurrent Neural Network applied to the simulated word exposure trajectories. Second, we consider word embeddings projected into a unified multilingual space. Third, we apply transfer learning on the English AoE regressor to improve the performance of non-English AoE regressors. We show that AoA lists across languages share inherent similarities that enable Machine Learning models to transfer insights from one language to another, thus diminishing the effect of the smaller sample sizes for non-English languages. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: Note Label: Notes Group: Note Data: https://github.com/readerbench/Age-of-Exposure/tree/master/resources – Name: CodeSource Label: IES Funded Group: SrcInfo Data: Yes – Name: DateEntry Label: Entry Date Group: Date Data: 2024 – Name: AN Label: Accession Number Group: ID Data: EJ1453674 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1453674 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s40593-023-00386-7 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1353 Subjects: – SubjectFull: Multilingualism Type: general – SubjectFull: English (Second Language) Type: general – SubjectFull: Second Language Learning Type: general – SubjectFull: Second Language Instruction Type: general – SubjectFull: Age Differences Type: general – SubjectFull: Semantics Type: general – SubjectFull: Word Lists Type: general – SubjectFull: Vocabulary Development Type: general – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Scores Type: general – SubjectFull: Simulation Type: general – SubjectFull: Networks Type: general – SubjectFull: Prediction Type: general – SubjectFull: Models Type: general – SubjectFull: Computational Linguistics Type: general – SubjectFull: Natural Language Processing Type: general – SubjectFull: Spanish Type: general – SubjectFull: French Type: general – SubjectFull: German Type: general Titles: – TitleFull: Multilingual Age of Exposure 2.0 Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Robert-Mihai Botarleanu – PersonEntity: Name: NameFull: Micah Watanabe – PersonEntity: Name: NameFull: Mihai Dascalu – PersonEntity: Name: NameFull: Scott A. Crossley – PersonEntity: Name: NameFull: Danielle S. McNamara IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1560-4292 – Type: issn-electronic Value: 1560-4306 Numbering: – Type: volume Value: 34 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Artificial Intelligence in Education Type: main |
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