<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="abdalla-etal-2023-makes">
    <titleInfo>
        <title>What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Mohamed</namePart>
        <namePart type="family">Abdalla</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Krishnapriya</namePart>
        <namePart type="family">Vishnubhotla</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Saif</namePart>
        <namePart type="family">Mohammad</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Isabelle</namePart>
            <namePart type="family">Augenstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dubrovnik, Croatia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The degree of semantic relatedness of two units of language has long been considered fundamental to understanding meaning. Additionally, automatically determining relatedness has many applications such as question answering and summarization. However, prior NLP work has largely focused on semantic similarity, a subset of relatedness, because of a lack of relatedness datasets. In this paper, we introduce a dataset for Semantic Textual Relatedness, STR-2022, that has 5,500 English sentence pairs manually annotated using a comparative annotation framework, resulting in fine-grained scores. We show that human intuition regarding relatedness of sentence pairs is highly reliable, with a repeat annotation correlation of 0.84. We use the dataset to explore questions on what makes sentences semantically related. We also show the utility of STR-2022 for evaluating automatic methods of sentence representation and for various downstream NLP tasks. Our dataset, data statement, and annotation questionnaire can be found at: https://doi.org/10.5281/zenodo.7599667.</abstract>
    <identifier type="citekey">abdalla-etal-2023-makes</identifier>
    <identifier type="doi">10.18653/v1/2023.eacl-main.55</identifier>
    <location>
        <url>https://aclanthology.org/2023.eacl-main.55</url>
    </location>
    <part>
        <date>2023-05</date>
        <extent unit="page">
            <start>782</start>
            <end>796</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study
%A Abdalla, Mohamed
%A Vishnubhotla, Krishnapriya
%A Mohammad, Saif
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F abdalla-etal-2023-makes
%X The degree of semantic relatedness of two units of language has long been considered fundamental to understanding meaning. Additionally, automatically determining relatedness has many applications such as question answering and summarization. However, prior NLP work has largely focused on semantic similarity, a subset of relatedness, because of a lack of relatedness datasets. In this paper, we introduce a dataset for Semantic Textual Relatedness, STR-2022, that has 5,500 English sentence pairs manually annotated using a comparative annotation framework, resulting in fine-grained scores. We show that human intuition regarding relatedness of sentence pairs is highly reliable, with a repeat annotation correlation of 0.84. We use the dataset to explore questions on what makes sentences semantically related. We also show the utility of STR-2022 for evaluating automatic methods of sentence representation and for various downstream NLP tasks. Our dataset, data statement, and annotation questionnaire can be found at: https://doi.org/10.5281/zenodo.7599667.
%R 10.18653/v1/2023.eacl-main.55
%U https://aclanthology.org/2023.eacl-main.55
%U https://doi.org/10.18653/v1/2023.eacl-main.55
%P 782-796

Markdown (Informal)

[What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study](https://aclanthology.org/2023.eacl-main.55) (Abdalla et al., EACL 2023)

• What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study (Abdalla et al., EACL 2023)

ACL

• Mohamed Abdalla, Krishnapriya Vishnubhotla, and Saif Mohammad. 2023. What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 782–796, Dubrovnik, Croatia. Association for Computational Linguistics.