<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mikhaylovskiy-2023-long">
    <titleInfo>
        <title>Long Story Generation Challenge</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Nikolay</namePart>
        <namePart type="family">Mikhaylovskiy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Mille</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Prague, Czechia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose a shared task of human-like long story generation, LSG Challenge, that asks models to output a consistent human-like long story (a Harry Potter generic audience fanfic in English), given a prompt of about 1K tokens. We suggest a novel statistical metric of the text structuredness, GloVe Autocorrelations Power/ Exponential Law Mean Absolute Percentage Error Ratio (GAPELMAPER) and the use of previously-known UNION metric and a human evaluation protocol. We hope that LSG can open new avenues for researchers to investigate sampling approaches, prompting strategies, autoregressive and non-autoregressive text generation architectures and break the barrier to generate consistent long (40K+ word) texts.</abstract>
    <identifier type="citekey">mikhaylovskiy-2023-long</identifier>
    <location>
        <url>https://aclanthology.org/2023.inlg-genchal.2</url>
    </location>
    <part>
        <date>2023-09</date>
        <extent unit="page">
            <start>10</start>
            <end>16</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Long Story Generation Challenge
%A Mikhaylovskiy, Nikolay
%Y Mille, Simon
%S Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F mikhaylovskiy-2023-long
%X We propose a shared task of human-like long story generation, LSG Challenge, that asks models to output a consistent human-like long story (a Harry Potter generic audience fanfic in English), given a prompt of about 1K tokens. We suggest a novel statistical metric of the text structuredness, GloVe Autocorrelations Power/ Exponential Law Mean Absolute Percentage Error Ratio (GAPELMAPER) and the use of previously-known UNION metric and a human evaluation protocol. We hope that LSG can open new avenues for researchers to investigate sampling approaches, prompting strategies, autoregressive and non-autoregressive text generation architectures and break the barrier to generate consistent long (40K+ word) texts.
%U https://aclanthology.org/2023.inlg-genchal.2
%P 10-16

Markdown (Informal)

[Long Story Generation Challenge](https://aclanthology.org/2023.inlg-genchal.2) (Mikhaylovskiy, INLG-SIGDIAL 2023)

• Long Story Generation Challenge (Mikhaylovskiy, INLG-SIGDIAL 2023)

ACL

• Nikolay Mikhaylovskiy. 2023. Long Story Generation Challenge. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges, pages 10–16, Prague, Czechia. Association for Computational Linguistics.