A Gold Standard to Measure Relative Linguistic Complexity with a Grounded Language Learning Model

Leonor Becerra-Bonache, Henning Christiansen, M. Dolores Jiménez-López

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Abstract

This paper focuses on linguistic complexity from a relative perspective. It presents a grounded language learning system that can be used to study linguistic complexity from a developmental point of view and introduces a tool for generating a gold standard in order to evaluate the performance of the learning system. In general, researchers agree that it is more feasible to approach complexity from an objective or theory-oriented viewpoint than from a subjective or user-related point of view. Studies that have adopted a relative complexity approach have showed some preferences for L2 learners. In this paper, we try to show that computational models of the process of language acquisition may be an important tool to consider children and the process of first language acquisition as suitable candidates for evaluating the complexity of languages.
OriginalsprogEngelsk
TitelProceedings of the Workshop on Linguistic Complexity and Natural Language Processing : A workshop at COLING 2018: The 27th International Conference on Computational Linguistics
RedaktørerLeonor Becerra-Bonache, M. Dolores Jiménez-López, Carlos Martín-Vide, Adrià Torrens-Urrutia
UdgivelsesstedSanta Fe
ForlagAssociation for Computational Linguistics
Publikationsdato2018
Sider1-9
ISBN (Trykt)9781948087629
StatusUdgivet - 2018
BegivenhedThe 27th International Conference on Computational Linguistics: Workshop on Linguistic Complexity and Natural Language Processing - Santa Fe Community Convention Center, Santa Fe, USA
Varighed: 20 aug. 201826 aug. 2018
http://coling2018.org

Konference

KonferenceThe 27th International Conference on Computational Linguistics
LokationSanta Fe Community Convention Center
Land/OmrådeUSA
BySanta Fe
Periode20/08/201826/08/2018
Internetadresse

Bibliografisk note

Electronic publication; no known ISBN / ISSN

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