<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="baruah-narayanan-2023-character">
    <titleInfo>
        <title>Character Coreference Resolution in Movie Screenplays</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sabyasachee</namePart>
        <namePart type="family">Baruah</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shrikanth</namePart>
        <namePart type="family">Narayanan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: ACL 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Rogers</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jordan</namePart>
            <namePart type="family">Boyd-Graber</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Naoaki</namePart>
            <namePart type="family">Okazaki</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Toronto, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Movie screenplays have a distinct narrative structure. It segments the story into scenes containing interleaving descriptions of actions, locations, and character dialogues.A typical screenplay spans several scenes and can include long-range dependencies between characters and events.A holistic document-level understanding of the screenplay requires several natural language processing capabilities, such as parsing, character identification, coreference resolution, action recognition, summarization, and attribute discovery. In this work, we develop scalable and robust methods to extract the structural information and character coreference clusters from full-length movie screenplays. We curate two datasets for screenplay parsing and character coreference — MovieParse and MovieCoref, respectively.We build a robust screenplay parser to handle inconsistencies in screenplay formatting and leverage the parsed output to link co-referring character mentions.Our coreference models can scale to long screenplay documents without drastically increasing their memory footprints.</abstract>
    <identifier type="citekey">baruah-narayanan-2023-character</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-acl.654</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-acl.654</url>
    </location>
    <part>
        <date>2023-07</date>
        <extent unit="page">
            <start>10300</start>
            <end>10313</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Character Coreference Resolution in Movie Screenplays
%A Baruah, Sabyasachee
%A Narayanan, Shrikanth
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F baruah-narayanan-2023-character
%X Movie screenplays have a distinct narrative structure. It segments the story into scenes containing interleaving descriptions of actions, locations, and character dialogues.A typical screenplay spans several scenes and can include long-range dependencies between characters and events.A holistic document-level understanding of the screenplay requires several natural language processing capabilities, such as parsing, character identification, coreference resolution, action recognition, summarization, and attribute discovery. In this work, we develop scalable and robust methods to extract the structural information and character coreference clusters from full-length movie screenplays. We curate two datasets for screenplay parsing and character coreference — MovieParse and MovieCoref, respectively.We build a robust screenplay parser to handle inconsistencies in screenplay formatting and leverage the parsed output to link co-referring character mentions.Our coreference models can scale to long screenplay documents without drastically increasing their memory footprints.
%R 10.18653/v1/2023.findings-acl.654
%U https://aclanthology.org/2023.findings-acl.654
%U https://doi.org/10.18653/v1/2023.findings-acl.654
%P 10300-10313

Markdown (Informal)

[Character Coreference Resolution in Movie Screenplays](https://aclanthology.org/2023.findings-acl.654) (Baruah & Narayanan, Findings 2023)

• Character Coreference Resolution in Movie Screenplays (Baruah & Narayanan, Findings 2023)

ACL

• Sabyasachee Baruah and Shrikanth Narayanan. 2023. Character Coreference Resolution in Movie Screenplays. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10300–10313, Toronto, Canada. Association for Computational Linguistics.