<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hao-etal-2020-probabilistic">
    <titleInfo>
        <title>Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yiding</namePart>
        <namePart type="family">Hao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Simon</namePart>
        <namePart type="family">Mendelsohn</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rachel</namePart>
        <namePart type="family">Sterneck</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Randi</namePart>
        <namePart type="family">Martinez</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Robert</namePart>
        <namePart type="family">Frank</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-11</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">Yohei</namePart>
            <namePart type="family">Oseki</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">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>By positing a relationship between naturalistic reading times and information-theoretic surprisal, surprisal theory (Hale, 2001; Levy, 2008) provides a natural interface between language models and psycholinguistic models. This paper re-evaluates a claim due to Goodkind and Bicknell (2018) that a language model’s ability to model reading times is a linear function of its perplexity. By extending Goodkind and Bicknell’s analysis to modern neural architectures, we show that the proposed relation does not always hold for Long Short-Term Memory networks, Transformers, and pre-trained models. We introduce an alternate measure of language modeling performance called predictability norm correlation based on Cloze probabilities measured from human subjects. Our new metric yields a more robust relationship between language model quality and psycholinguistic modeling performance that allows for comparison between models with different training configurations.</abstract>
    <identifier type="citekey">hao-etal-2020-probabilistic</identifier>
    <identifier type="doi">10.18653/v1/2020.cmcl-1.10</identifier>
    <location>
        <url>https://aclanthology.org/2020.cmcl-1.10</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>75</start>
            <end>86</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling
%A Hao, Yiding
%A Mendelsohn, Simon
%A Sterneck, Rachel
%A Martinez, Randi
%A Frank, Robert
%Y Chersoni, Emmanuele
%Y Jacobs, Cassandra
%Y Oseki, Yohei
%Y Prévot, Laurent
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F hao-etal-2020-probabilistic
%X By positing a relationship between naturalistic reading times and information-theoretic surprisal, surprisal theory (Hale, 2001; Levy, 2008) provides a natural interface between language models and psycholinguistic models. This paper re-evaluates a claim due to Goodkind and Bicknell (2018) that a language model’s ability to model reading times is a linear function of its perplexity. By extending Goodkind and Bicknell’s analysis to modern neural architectures, we show that the proposed relation does not always hold for Long Short-Term Memory networks, Transformers, and pre-trained models. We introduce an alternate measure of language modeling performance called predictability norm correlation based on Cloze probabilities measured from human subjects. Our new metric yields a more robust relationship between language model quality and psycholinguistic modeling performance that allows for comparison between models with different training configurations.
%R 10.18653/v1/2020.cmcl-1.10
%U https://aclanthology.org/2020.cmcl-1.10
%U https://doi.org/10.18653/v1/2020.cmcl-1.10
%P 75-86

Markdown (Informal)

[Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling](https://aclanthology.org/2020.cmcl-1.10) (Hao et al., CMCL 2020)

• Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling (Hao et al., CMCL 2020)

ACL

• Yiding Hao, Simon Mendelsohn, Rachel Sterneck, Randi Martinez, and Robert Frank. 2020. Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 75–86, Online. Association for Computational Linguistics.