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        <title>A Multinomial Processing Tree Model of RC Attachment</title>
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            <namePart type="given">Emmanuele</namePart>
            <namePart type="family">Chersoni</namePart>
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            <namePart type="given">Nora</namePart>
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            <namePart type="given">Cassandra</namePart>
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        <name type="personal">
            <namePart type="given">Enrico</namePart>
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    <abstract>In the field of sentence processing, speakers’ preferred interpretation of ambiguous sentences are often determined using a variant of a discrete choice task, in which participants are asked to indicate their preferred meaning of an ambiguous sentence. We discuss participants’ degree of attentiveness as a potential source of bias and variability in such tasks. We show that it may distort the estimates of the preference of a particular interpretation obtained in such experiments and may thus complicate the interpretation of the results as well as the comparison of the results of several experiments. We propose an analysis method based on multinomial processing tree models (Batchelder and Riefer, 1999) which can correct for this bias and allows for a separation of parameters of theoretical importance from nuisance parameters. We test two variants of the MPT-based model on experimental data from English and Turkish and demonstrate that our method can provide deeper insight into the processes underlying participants’ answering behavior and their interpretation preferences than an analysis based on raw percentages.</abstract>
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    <identifier type="doi">10.18653/v1/2021.cmcl-1.4</identifier>
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%0 Conference Proceedings
%T A Multinomial Processing Tree Model of RC Attachment
%A Logacev, Pavel
%A Dokudan, Noyan
%Y Chersoni, Emmanuele
%Y Hollenstein, Nora
%Y Jacobs, Cassandra
%Y Oseki, Yohei
%Y Prévot, Laurent
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F logacev-dokudan-2021-multinomial
%X In the field of sentence processing, speakers’ preferred interpretation of ambiguous sentences are often determined using a variant of a discrete choice task, in which participants are asked to indicate their preferred meaning of an ambiguous sentence. We discuss participants’ degree of attentiveness as a potential source of bias and variability in such tasks. We show that it may distort the estimates of the preference of a particular interpretation obtained in such experiments and may thus complicate the interpretation of the results as well as the comparison of the results of several experiments. We propose an analysis method based on multinomial processing tree models (Batchelder and Riefer, 1999) which can correct for this bias and allows for a separation of parameters of theoretical importance from nuisance parameters. We test two variants of the MPT-based model on experimental data from English and Turkish and demonstrate that our method can provide deeper insight into the processes underlying participants’ answering behavior and their interpretation preferences than an analysis based on raw percentages.
%R 10.18653/v1/2021.cmcl-1.4
%U https://aclanthology.org/2021.cmcl-1.4
%U https://doi.org/10.18653/v1/2021.cmcl-1.4
%P 39-47

Markdown (Informal)

[A Multinomial Processing Tree Model of RC Attachment](https://aclanthology.org/2021.cmcl-1.4) (Logacev & Dokudan, CMCL 2021)

• A Multinomial Processing Tree Model of RC Attachment (Logacev & Dokudan, CMCL 2021)

ACL

• Pavel Logacev and Noyan Dokudan. 2021. A Multinomial Processing Tree Model of RC Attachment. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 39–47, Online. Association for Computational Linguistics.