Status : Doctorante
LLF, CNRS – UMR 7110
Université Paris Diderot-Paris 7
Case 7031 – 5, rue Thomas Mann,
75205 Paris cedex 13
E-mail : email@example.com
Resume : Maureen de Seyssel_en.pdf (106 Ko)
Title : Does multilingual input help or hinder early language acquisition? A computational modelling approach
Inscription : 2020 à École normale Supérieure
Experimental studies in bilingual language acquisition are based on the assumption that children separate languages at birth or within months, and that this early ability is essential for successful learning. This would prevent children from mixing languages and learning a multilingual representation that does not correspond to any specific language.
This project will test this hypothesis following a reverse-engineering approach by using computational models, which aim to model the ideal learner when faced with input data whose number of languages is a priori unknown. This approach will directly test two aspects of the hypothesis : (1) the premise that it is possible to separate languages before learning them, and (2) the justification that separation is necessary for learning several languages in parallel. Regarding point 2, we will assess the impact of the nature of multilingual input data, ranging from segregation by speaker (one parent, one language), to total mixing, possibly including code switching, on the performance of a statistical language learning model. For both points, we will take into account the distance between languages.