Villavicencio, A. orcid.org/0000-0002-3731-9168, Idiart, M., Berwick, R. et al. (1 more author) (2013) Language acquisition and probabilistic models : keeping it simple. In: Schuetze, H., Fung, P. and Poesio, M., (eds.) Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. 51st Annual Meeting of the Association for Computational Linguistics, 04-09 Aug 2013, Sofia, Bulgaria. Association for Computational Linguistics (ACL) , pp. 1321-1330. ISBN 9781937284503
Abstract
Hierarchical Bayesian Models (HBMs) have been used with some success to capture empirically observed patterns of under- and overgeneralization in child language acquisition. However, as is well known, HBMs are "ideal" learning systems, assuming access to unlimited computational resources that may not be available to child language learners. Consequently, it remains crucial to carefully assess the use of HBMs along with alternative, possibly simpler, candidate models. This paper presents such an evaluation for a language acquisition domain where explicit HBMshave been proposed: the acquisition of English dative constructions. In particular, we present a detailed, empiricallygrounded model-selection comparison of HBMs vs. a simpler alternative based on clustering along with maximum likelihood estimation that we call linear competition learning (LCL). Our results demonstrate that LCL can match HBM model performance without incurring on the high computational costs associated with HBMs.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2013 Association for Computational Linguistics. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Nov 2019 11:31 |
Last Modified: | 25 Nov 2019 11:31 |
Published Version: | https://www.aclweb.org/anthology/P13-1130/ |
Status: | Published |
Publisher: | Association for Computational Linguistics (ACL) |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153573 |