<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="brahman-chaturvedi-2020-modeling">
    <titleInfo>
        <title>Modeling Protagonist Emotions for Emotion-Aware Storytelling</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Faeze</namePart>
        <namePart type="family">Brahman</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Snigdha</namePart>
        <namePart type="family">Chaturvedi</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 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yang</namePart>
            <namePart type="family">Liu</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>Emotions and their evolution play a central role in creating a captivating story. In this paper, we present the first study on modeling the emotional trajectory of the protagonist in neural storytelling. We design methods that generate stories that adhere to given story titles and desired emotion arcs for the protagonist. Our models include Emotion Supervision (EmoSup) and two Emotion-Reinforced (EmoRL) models. The EmoRL models use special rewards designed to regularize the story generation process through reinforcement learning. Our automatic and manual evaluations demonstrate that these models are significantly better at generating stories that follow the desired emotion arcs compared to baseline methods, without sacrificing story quality.</abstract>
    <identifier type="citekey">brahman-chaturvedi-2020-modeling</identifier>
    <identifier type="doi">10.18653/v1/2020.emnlp-main.426</identifier>
    <location>
        <url>https://aclanthology.org/2020.emnlp-main.426</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>5277</start>
            <end>5294</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Modeling Protagonist Emotions for Emotion-Aware Storytelling
%A Brahman, Faeze
%A Chaturvedi, Snigdha
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F brahman-chaturvedi-2020-modeling
%X Emotions and their evolution play a central role in creating a captivating story. In this paper, we present the first study on modeling the emotional trajectory of the protagonist in neural storytelling. We design methods that generate stories that adhere to given story titles and desired emotion arcs for the protagonist. Our models include Emotion Supervision (EmoSup) and two Emotion-Reinforced (EmoRL) models. The EmoRL models use special rewards designed to regularize the story generation process through reinforcement learning. Our automatic and manual evaluations demonstrate that these models are significantly better at generating stories that follow the desired emotion arcs compared to baseline methods, without sacrificing story quality.
%R 10.18653/v1/2020.emnlp-main.426
%U https://aclanthology.org/2020.emnlp-main.426
%U https://doi.org/10.18653/v1/2020.emnlp-main.426
%P 5277-5294

Markdown (Informal)

[Modeling Protagonist Emotions for Emotion-Aware Storytelling](https://aclanthology.org/2020.emnlp-main.426) (Brahman & Chaturvedi, EMNLP 2020)

• Modeling Protagonist Emotions for Emotion-Aware Storytelling (Brahman & Chaturvedi, EMNLP 2020)

ACL

• Faeze Brahman and Snigdha Chaturvedi. 2020. Modeling Protagonist Emotions for Emotion-Aware Storytelling. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5277–5294, Online. Association for Computational Linguistics.