<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yan-white-2019-framework">
    <titleInfo>
        <title>A Framework for Decoding Event-Related Potentials from Text</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Shaorong</namePart>
        <namePart type="family">Yan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Aaron</namePart>
        <namePart type="given">Steven</namePart>
        <namePart type="family">White</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Emmanuele</namePart>
            <namePart type="family">Chersoni</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Cassandra</namePart>
            <namePart type="family">Jacobs</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alessandro</namePart>
            <namePart type="family">Lenci</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tal</namePart>
            <namePart type="family">Linzen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Laurent</namePart>
            <namePart type="family">Prévot</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Enrico</namePart>
            <namePart type="family">Santus</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Minneapolis, Minnesota</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose a novel framework for modeling event-related potentials (ERPs) collected during reading that couples pre-trained convolutional decoders with a language model. Using this framework, we compare the abilities of a variety of existing and novel sentence processing models to reconstruct ERPs. We find that modern contextual word embeddings underperform surprisal-based models but that, combined, the two outperform either on its own.</abstract>
    <identifier type="citekey">yan-white-2019-framework</identifier>
    <identifier type="doi">10.18653/v1/W19-2910</identifier>
    <location>
        <url>https://aclanthology.org/W19-2910/</url>
    </location>
    <part>
        <date>2019-06</date>
        <extent unit="page">
            <start>86</start>
            <end>92</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Framework for Decoding Event-Related Potentials from Text
%A Yan, Shaorong
%A White, Aaron Steven
%Y Chersoni, Emmanuele
%Y Jacobs, Cassandra
%Y Lenci, Alessandro
%Y Linzen, Tal
%Y Prévot, Laurent
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F yan-white-2019-framework
%X We propose a novel framework for modeling event-related potentials (ERPs) collected during reading that couples pre-trained convolutional decoders with a language model. Using this framework, we compare the abilities of a variety of existing and novel sentence processing models to reconstruct ERPs. We find that modern contextual word embeddings underperform surprisal-based models but that, combined, the two outperform either on its own.
%R 10.18653/v1/W19-2910
%U https://aclanthology.org/W19-2910/
%U https://doi.org/10.18653/v1/W19-2910
%P 86-92

Markdown (Informal)

[A Framework for Decoding Event-Related Potentials from Text](https://aclanthology.org/W19-2910/) (Yan & White, CMCL 2019)

• A Framework for Decoding Event-Related Potentials from Text (Yan & White, CMCL 2019)

ACL

• Shaorong Yan and Aaron Steven White. 2019. A Framework for Decoding Event-Related Potentials from Text. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 86–92, Minneapolis, Minnesota. Association for Computational Linguistics.