Modelling verb selection within argument structure constructions

Matusevych, Yevgen; Alishahi, Afra; Backus, Ad

doi:10.1080/23273798.2016.1200732

Cited by 4 publications

(4 citation statements)

References 86 publications

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“…Our findings confirmed the previous literature (Ellis and Ferreira-Junior, 2009;Matusevych et al, 2016) that input frequency determines its production frequency in the L2. Cue availability made an equally high contribution to L2 production frequency at the two levels of L2 competence.…”

Section: Discussionsupporting

confidence: 93%

Modeling Input Factors in Second Language Acquisition of the English Article Construction

Zhao

Fan

2021

Front. Psychol.

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Based on the Competition Model, the current study investigated how cue availability and cue reliability as two important input factors influenced second language (L2) learners' cue learning of the English article construction. Written corpus data of university-level Chinese-L1 learners of English were sampled for a comparison of English majors and non-English majors who demonstrated two levels of L2 competence in English article usage. The path model analysis in structural equation modeling was utilized to investigate the relationship between the input factors and L2 usage (frequency and accuracy of article cue production). The findings contribute novel and scarce empirical evidence that confirms a central claim of the Competition Model, i.e., the changing importance of cue availability and cue reliability in the frequency and accuracy of production. Cue availability was found to determine L2 production frequency regardless of level of L2 competence. Cue reliability was the input factor that differentiated competence levels. When learners stayed at a relatively lower L2 proficiency, cue reliability played an important role in influencing L2 frequency of usage rather than accuracy of usage. When learners developed increased exposure to and stronger competence in the target language, cue reliability played a significant role in determining learners' success of cue learning. The study is methodologically innovative and expands the empirical applicability of the Competition Model to the domain of second language production and construction learning.

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Section: Discussionsupporting

confidence: 93%

Modeling Input Factors in Second Language Acquisition of the English Article Construction

Zhao

Fan

2021

Front. Psychol.

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“…To address this gap in the literature, in this article we use a computational model of learning argument structure constructions from bilingual input. We choose this model, because it has been used for simulating bilingual learning of argument structure constructions (Matusevych, Alishahi, & Backus, 2016b, 2017, and it allows for measuring the amount of CLI in this domain. Our goal in the present study is to demonstrate how the amount of CLI can be measured in the learning and use of such constructions, and how a CLI measure can be employed to explain the patterns of language use observed in the model.…”

Section: Quantifying Cross-linguistic Influencementioning

confidence: 99%

“…The idea comes from the study of Anderson (1991), who implemented the model of human category learning. Stevenson (2008, 2010) were the first to simulate the learning of argument structure constructions using the same algorithm, while Matusevych et al (2016bMatusevych et al ( , 2017 adapted the model for bilingual learning. In all the mentioned studies, the model was demonstrated to replicate certain types of human behavior in the respective domain.…”

Section: Model Overviewmentioning

confidence: 99%

“…In this study, we only employed a particular linguistic task related to sentence interpretation, but it is perfectly possible to use this computational model for simulating other linguistic tasks: filling in verbs or prepositions, verb definition, verb selection, and others (see Matusevych et al, 2016bMatusevych et al, , 2017. For all these tasks, the same type of measure (CLI or ∆CLI) can be used to quantify the amount of CLI and shed light on its role in language comprehension and production.…”

Section: Quantifying the Effect Of CLImentioning

confidence: 99%

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Quantifying cross-linguistic influence with a computational model

Matusevych

Alishahi

Backus

2018

LAB

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Cross-linguistic influence (CLI) is one of the key phenomena in bilingual and second language learning. We propose a method for quantifying CLI in the use of linguistic constructions with the help of a computational model, which acquires constructions in two languages from bilingual input. We focus on the acquisition of case-marking cues in Russian and German and simulate two experiments that employ a picture-choice task tapping into the mechanisms of sentence interpretation. Our model yields behavioral patterns similar to human, and these patterns can be explained by the amount of CLI: the negative CLI in high amounts leads to the misinterpretation of participant roles in Russian and German object-verb-subject sentences. Finally, we make two novel predictions about the acquisition of case-marking cues in Russian and German. Most importantly, our simulations suggest that the high degree of positive CLI may facilitate the interpretation of object-verb-subject sentences.

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Simulated Learners in Educational Technology: A Systematic Literature Review and a Turing-like Test

Käser

Alexandron

2023

Int J Artif Intell Educ

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Modelling verb selection within argument structure constructions

Cited by 4 publications

References 86 publications

Modeling Input Factors in Second Language Acquisition of the English Article Construction

Modeling Input Factors in Second Language Acquisition of the English Article Construction

Quantifying cross-linguistic influence with a computational model

Simulated Learners in Educational Technology: A Systematic Literature Review and a Turing-like Test

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