<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shain-schuler-2018-deconvolutional">
    <titleInfo>
        <title>Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Cory</namePart>
        <namePart type="family">Shain</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">William</namePart>
        <namePart type="family">Schuler</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-oct-nov</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Brussels, Belgium</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Researchers in computational psycholinguistics frequently use linear models to study time series data generated by human subjects. However, time series may violate the assumptions of these models through temporal diffusion, where stimulus presentation has a lingering influence on the response as the rest of the experiment unfolds. This paper proposes a new statistical model that borrows from digital signal processing by recasting the predictors and response as convolutionally-related signals, using recent advances in machine learning to fit latent impulse response functions (IRFs) of arbitrary shape. A synthetic experiment shows successful recovery of true latent IRFs, and psycholinguistic experiments reveal plausible, replicable, and fine-grained estimates of latent temporal dynamics, with comparable or improved prediction quality to widely-used alternatives.</abstract>
    <identifier type="citekey">shain-schuler-2018-deconvolutional</identifier>
    <identifier type="doi">10.18653/v1/D18-1288</identifier>
    <location>
        <url>https://aclanthology.org/D18-1288</url>
    </location>
    <part>
        <date>2018-oct-nov</date>
        <extent unit="page">
            <start>2679</start>
            <end>2689</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects
%A Shain, Cory
%A Schuler, William
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F shain-schuler-2018-deconvolutional
%X Researchers in computational psycholinguistics frequently use linear models to study time series data generated by human subjects. However, time series may violate the assumptions of these models through temporal diffusion, where stimulus presentation has a lingering influence on the response as the rest of the experiment unfolds. This paper proposes a new statistical model that borrows from digital signal processing by recasting the predictors and response as convolutionally-related signals, using recent advances in machine learning to fit latent impulse response functions (IRFs) of arbitrary shape. A synthetic experiment shows successful recovery of true latent IRFs, and psycholinguistic experiments reveal plausible, replicable, and fine-grained estimates of latent temporal dynamics, with comparable or improved prediction quality to widely-used alternatives.
%R 10.18653/v1/D18-1288
%U https://aclanthology.org/D18-1288
%U https://doi.org/10.18653/v1/D18-1288
%P 2679-2689

Markdown (Informal)

[Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects](https://aclanthology.org/D18-1288) (Shain & Schuler, EMNLP 2018)

• Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects (Shain & Schuler, EMNLP 2018)

ACL

• Cory Shain and William Schuler. 2018. Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2679–2689, Brussels, Belgium. Association for Computational Linguistics.