<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="maimon-2025-novel">
    <titleInfo>
        <title>A Novel Computational Modeling Foundation for Automatic Coherence Assessment</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Aviya</namePart>
        <namePart type="family">Maimon</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Luis</namePart>
            <namePart type="family">Chiruzzo</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lu</namePart>
            <namePart type="family">Wang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Albuquerque, New Mexico</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-189-6</identifier>
    </relatedItem>
    <abstract>Coherence is an essential property of well-written texts, that refers to the way textual units relate to one another. In the era of generative AI, coherence assessment is essential for many NLP tasks such as summarization, long-form question-answering, and more.Current NLP approaches for modeling coherence often rely on a proxy task, specifically, sentence reordering. However, such an approach may not capture the full range of factors contributing to coherence.To remedy this, in this work we employ the formal linguistic definition by Reinhart:1980 of what makes a discourse coherent, consisting of three conditions, cohesion, consistency and relevance, and formalize these conditions as respective computational tasks, which are in turn jointly trained. We evaluate this modeling approach on two human-rated coherence benchmarks: one of automatically-generated stories and one of real-world texts.Our experiments show that jointly training on the proposed tasks leads to better performance on each task compared with task-specific models, and to better performance on assessing coherence overall.Our proposed computational framework thus paves the way for a more advanced, broad-coverage coherence assessment.</abstract>
    <identifier type="citekey">maimon-2025-novel</identifier>
    <identifier type="doi">10.18653/v1/2025.naacl-long.277</identifier>
    <location>
        <url>https://aclanthology.org/2025.naacl-long.277/</url>
    </location>
    <part>
        <date>2025-04</date>
        <extent unit="page">
            <start>5359</start>
            <end>5377</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Novel Computational Modeling Foundation for Automatic Coherence Assessment
%A Maimon, Aviya
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F maimon-2025-novel
%X Coherence is an essential property of well-written texts, that refers to the way textual units relate to one another. In the era of generative AI, coherence assessment is essential for many NLP tasks such as summarization, long-form question-answering, and more.Current NLP approaches for modeling coherence often rely on a proxy task, specifically, sentence reordering. However, such an approach may not capture the full range of factors contributing to coherence.To remedy this, in this work we employ the formal linguistic definition by Reinhart:1980 of what makes a discourse coherent, consisting of three conditions, cohesion, consistency and relevance, and formalize these conditions as respective computational tasks, which are in turn jointly trained. We evaluate this modeling approach on two human-rated coherence benchmarks: one of automatically-generated stories and one of real-world texts.Our experiments show that jointly training on the proposed tasks leads to better performance on each task compared with task-specific models, and to better performance on assessing coherence overall.Our proposed computational framework thus paves the way for a more advanced, broad-coverage coherence assessment.
%R 10.18653/v1/2025.naacl-long.277
%U https://aclanthology.org/2025.naacl-long.277/
%U https://doi.org/10.18653/v1/2025.naacl-long.277
%P 5359-5377

Markdown (Informal)

[A Novel Computational Modeling Foundation for Automatic Coherence Assessment](https://aclanthology.org/2025.naacl-long.277/) (Maimon, NAACL 2025)

• A Novel Computational Modeling Foundation for Automatic Coherence Assessment (Maimon, NAACL 2025)

ACL

• Aviya Maimon. 2025. A Novel Computational Modeling Foundation for Automatic Coherence Assessment. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5359–5377, Albuquerque, New Mexico. Association for Computational Linguistics.