<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhu-etal-2023-nlp">
    <titleInfo>
        <title>Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Lixing</namePart>
        <namePart type="family">Zhu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Runcong</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lin</namePart>
        <namePart type="family">Gui</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yulan</namePart>
        <namePart type="family">He</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Narrative understanding involves capturing the author’s cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author’s thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct a comprehensive survey of narrative understanding tasks, thoroughly examining their key features, definitions, taxonomy, associated datasets, training objectives, evaluation metrics, and limitations. Furthermore, we explore the potential of expanding the capabilities of modularized LLMs to address novel narrative understanding tasks. By framing narrative understanding as the retrieval of the author’s imaginative cues that outline the narrative structure, our study introduces a fresh perspective on enhancing narrative comprehension.</abstract>
    <identifier type="citekey">zhu-etal-2023-nlp</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.677</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.677</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>10098</start>
            <end>10121</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding
%A Zhu, Lixing
%A Zhao, Runcong
%A Gui, Lin
%A He, Yulan
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhu-etal-2023-nlp
%X Narrative understanding involves capturing the author’s cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author’s thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct a comprehensive survey of narrative understanding tasks, thoroughly examining their key features, definitions, taxonomy, associated datasets, training objectives, evaluation metrics, and limitations. Furthermore, we explore the potential of expanding the capabilities of modularized LLMs to address novel narrative understanding tasks. By framing narrative understanding as the retrieval of the author’s imaginative cues that outline the narrative structure, our study introduces a fresh perspective on enhancing narrative comprehension.
%R 10.18653/v1/2023.findings-emnlp.677
%U https://aclanthology.org/2023.findings-emnlp.677
%U https://doi.org/10.18653/v1/2023.findings-emnlp.677
%P 10098-10121

Markdown (Informal)

[Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding](https://aclanthology.org/2023.findings-emnlp.677) (Zhu et al., Findings 2023)

• Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding (Zhu et al., Findings 2023)

ACL

• Lixing Zhu, Runcong Zhao, Lin Gui, and Yulan He. 2023. Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10098–10121, Singapore. Association for Computational Linguistics.