<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dugan-etal-2020-roft">
    <titleInfo>
        <title>RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Liam</namePart>
        <namePart type="family">Dugan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Daphne</namePart>
        <namePart type="family">Ippolito</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Arun</namePart>
        <namePart type="family">Kirubarajan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chris</namePart>
        <namePart type="family">Callison-Burch</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Qun</namePart>
            <namePart type="family">Liu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Schlangen</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>In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text. However, the tasks of evaluating quality differences between NLG systems and understanding how humans perceive the generated text remain both crucial and difficult. In this system demonstration, we present Real or Fake Text (RoFT), a website that tackles both of these challenges by inviting users to try their hand at detecting machine-generated text in a variety of domains. We introduce a novel evaluation task based on detecting the boundary at which a text passage that starts off human-written transitions to being machine-generated. We show preliminary results of using RoFT to evaluate detection of machine-generated news articles.</abstract>
    <identifier type="citekey">dugan-etal-2020-roft</identifier>
    <identifier type="doi">10.18653/v1/2020.emnlp-demos.25</identifier>
    <location>
        <url>https://aclanthology.org/2020.emnlp-demos.25</url>
    </location>
    <part>
        <date>2020-10</date>
        <extent unit="page">
            <start>189</start>
            <end>196</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text
%A Dugan, Liam
%A Ippolito, Daphne
%A Kirubarajan, Arun
%A Callison-Burch, Chris
%Y Liu, Qun
%Y Schlangen, David
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2020
%8 October
%I Association for Computational Linguistics
%C Online
%F dugan-etal-2020-roft
%X In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text. However, the tasks of evaluating quality differences between NLG systems and understanding how humans perceive the generated text remain both crucial and difficult. In this system demonstration, we present Real or Fake Text (RoFT), a website that tackles both of these challenges by inviting users to try their hand at detecting machine-generated text in a variety of domains. We introduce a novel evaluation task based on detecting the boundary at which a text passage that starts off human-written transitions to being machine-generated. We show preliminary results of using RoFT to evaluate detection of machine-generated news articles.
%R 10.18653/v1/2020.emnlp-demos.25
%U https://aclanthology.org/2020.emnlp-demos.25
%U https://doi.org/10.18653/v1/2020.emnlp-demos.25
%P 189-196

Markdown (Informal)

[RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text](https://aclanthology.org/2020.emnlp-demos.25) (Dugan et al., EMNLP 2020)

• RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text (Dugan et al., EMNLP 2020)

ACL

• Liam Dugan, Daphne Ippolito, Arun Kirubarajan, and Chris Callison-Burch. 2020. RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 189–196, Online. Association for Computational Linguistics.