<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="poerner-etal-2020-sentence">
    <titleInfo>
        <title>Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Nina</namePart>
        <namePart type="family">Poerner</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ulli</namePart>
        <namePart type="family">Waltinger</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hinrich</namePart>
        <namePart type="family">Schütze</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joyce</namePart>
            <namePart type="family">Chai</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Natalie</namePart>
            <namePart type="family">Schluter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</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>We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings. We apply, extend and evaluate different meta-embedding methods from the word embedding literature at the sentence level, including dimensionality reduction (Yin and Schütze, 2016), generalized Canonical Correlation Analysis (Rastogi et al., 2015) and cross-view auto-encoders (Bollegala and Bao, 2018). Our sentence meta-embeddings set a new unsupervised State of The Art (SoTA) on the STS Benchmark and on the STS12-STS16 datasets, with gains of between 3.7% and 6.4% Pearson’s r over single-source systems.</abstract>
    <identifier type="citekey">poerner-etal-2020-sentence</identifier>
    <identifier type="doi">10.18653/v1/2020.acl-main.628</identifier>
    <location>
        <url>https://aclanthology.org/2020.acl-main.628</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>7027</start>
            <end>7034</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity
%A Poerner, Nina
%A Waltinger, Ulli
%A Schütze, Hinrich
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F poerner-etal-2020-sentence
%X We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings. We apply, extend and evaluate different meta-embedding methods from the word embedding literature at the sentence level, including dimensionality reduction (Yin and Schütze, 2016), generalized Canonical Correlation Analysis (Rastogi et al., 2015) and cross-view auto-encoders (Bollegala and Bao, 2018). Our sentence meta-embeddings set a new unsupervised State of The Art (SoTA) on the STS Benchmark and on the STS12-STS16 datasets, with gains of between 3.7% and 6.4% Pearson’s r over single-source systems.
%R 10.18653/v1/2020.acl-main.628
%U https://aclanthology.org/2020.acl-main.628
%U https://doi.org/10.18653/v1/2020.acl-main.628
%P 7027-7034

Markdown (Informal)

[Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity](https://aclanthology.org/2020.acl-main.628) (Poerner et al., ACL 2020)

• Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity (Poerner et al., ACL 2020)

ACL

• Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2020. Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7027–7034, Online. Association for Computational Linguistics.