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Subject–Verb Number Agreement in Bilingual Processing: (Lack of) Age of Acquisition and Proficiency Effects
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In: Languages; Volume 7; Issue 1; Pages: 15 (2022)
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Native sensitivity to subject-verb agreement violations in canonical and non-canonical Italian sentences ...
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KNOWLEDGE ON SUBJECT-VERB AGREEMENT OF GRADE 7 STUDENTS: BASIS FOR REMEDIAL TEACHING ...
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KNOWLEDGE ON SUBJECT-VERB AGREEMENT OF GRADE 7 STUDENTS: BASIS FOR REMEDIAL TEACHING ...
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Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones?
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Abstract:
The main subject and the associated verb in English must agree in grammatical number as per the Subject-Verb Agreement (SVA) phenomenon. It has been found that the presence of a noun between the verb and the main subject, whose grammatical number is opposite to that of the main subject, can cause speakers to produce a verb that agrees with the intervening noun rather than the main noun; the former thus acts as an agreement attractor. Such attractors have also been shown to pose a challenge for RNN models without explicit hierarchical bias to perform well on SVA tasks. Previous work suggests that syntactic cues in the input can aid such models to choose hierarchical rules over linear rules for number agreement. In this work, we investigate the effects of the choice of training data, training algorithm, and architecture on hierarchical generalization. We observe that the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on natural sentences with at least one attractor. Even in the presence of this biased training set, implicit hierarchical bias in the architecture (as in the Ordered Neurons LSTM) is not enough to capture syntax-sensitive dependencies. These results suggest that current RNNs do not capture the underlying hierarchical rules of natural language, but rather use shallower heuristics for their predictions.
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Keyword:
Artificial Intelligence and Robotics; Computational Linguistics; generalisation; Psycholinguistics and Neurolinguistics; RNN models; subject-verb agreement
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URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1228&context=scil
https://scholarworks.umass.edu/scil/vol4/iss1/38
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Look at that! BERT can be easily distracted from paying attention to morphosyntax
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Linguistic Outcomes of the Wayuunaiki-Spanish Language Contact Situation ...
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Object attraction and the role of structural hierarchy: evidence from Persian
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In: ISSN: 2397-1835 ; Glossa : A Journal of General Linguistics, Vol. 5, No 1 (2020) P. 27 (2020)
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Linguistic Outcomes of the Wayuunaiki-Spanish Language Contact Situation
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On the integration of linguistic features into statistical and neural machine translation
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In: Vanmassenhove, Eva Odette Jef orcid:0000-0003-1162-820X (2019) On the integration of linguistic features into statistical and neural machine translation. PhD thesis, Dublin City University. (2019)
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Elicited Subject-Verb Agreement in German Early L2 Children with Developmental Language Disorders
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In: Journal of the European Second Language Association; Vol 3, No 1 (2019); 46–57 ; 2399-9101 (2019)
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The Syntax, Semantics and Processing of Agreement and Binding Grammatical Illusions
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Eliciting ERP components for morphosyntactic agreement mismatches in perfectly grammatical sentences
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Infinitive verbs, verbal agreement and perceived competence / Verbos infinitivos, concordância verbal e competência percebida
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In: Revista de Estudos da Linguagem, Vol 27, Iss 4, Pp 1671-1700 (2019) (2019)
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Intervention effects in non-local dependencies: evidence from Persian
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The distribution of φ-probes in the inflectional structure
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In: Proceedings of the Linguistic Society of America; Vol 3 (2018): Proceedings of the Linguistic Society of America; 59:1–15 ; 2473-8689 (2018)
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Probes and pronouns: variation in agreement and clitic doubling in Arabic
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