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        <title>Learning an Input Filter for Argument Structure Acquisition</title>
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            <title>Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2017)</title>
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            <namePart type="given">Ted</namePart>
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            <namePart type="given">Tal</namePart>
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    <abstract>How do children learn a verb’s argument structure when their input contains nonbasic clauses that obscure verb transitivity? Here we present a new model that infers verb transitivity by learning to filter out non-basic clauses that were likely parsed in error. In simulations with child-directed speech, we show that this model accurately categorizes the majority of 50 frequent transitive, intransitive and alternating verbs, and jointly learns appropriate parameters for filtering parsing errors. Our model is thus able to filter out problematic data for verb learning without knowing in advance which data need to be filtered.</abstract>
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%0 Conference Proceedings
%T Learning an Input Filter for Argument Structure Acquisition
%A Perkins, Laurel
%A Feldman, Naomi
%A Lidz, Jeffrey
%Y Gibson, Ted
%Y Linzen, Tal
%Y Sayeed, Asad
%Y van Schijndel, Martin
%Y Schuler, William
%S Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2017)
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F perkins-etal-2017-learning
%X How do children learn a verb’s argument structure when their input contains nonbasic clauses that obscure verb transitivity? Here we present a new model that infers verb transitivity by learning to filter out non-basic clauses that were likely parsed in error. In simulations with child-directed speech, we show that this model accurately categorizes the majority of 50 frequent transitive, intransitive and alternating verbs, and jointly learns appropriate parameters for filtering parsing errors. Our model is thus able to filter out problematic data for verb learning without knowing in advance which data need to be filtered.
%R 10.18653/v1/W17-0702
%U https://aclanthology.org/W17-0702
%U https://doi.org/10.18653/v1/W17-0702
%P 11-19

Markdown (Informal)

[Learning an Input Filter for Argument Structure Acquisition](https://aclanthology.org/W17-0702) (Perkins et al., CMCL 2017)

• Learning an Input Filter for Argument Structure Acquisition (Perkins et al., CMCL 2017)

ACL

• Laurel Perkins, Naomi Feldman, and Jeffrey Lidz. 2017. Learning an Input Filter for Argument Structure Acquisition. In Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2017), pages 11–19, Valencia, Spain. Association for Computational Linguistics.