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        <title>Evaluation Datasets for Cross-lingual Semantic Textual Similarity</title>
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    <name type="personal">
        <namePart type="given">Tomáš</namePart>
        <namePart type="family">Hercig</namePart>
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        <namePart type="given">Pavel</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Semantic textual similarity (STS) systems estimate the degree of the meaning similarity between two sentences. Cross-lingual STS systems estimate the degree of the meaning similarity between two sentences, each in a different language. State-of-the-art algorithms usually employ a strongly supervised, resource-rich approach difficult to use for poorly-resourced languages. However, any approach needs to have evaluation data to confirm the results. In order to simplify the evaluation process for poorly-resourced languages (in terms of STS evaluation datasets), we present new datasets for cross-lingual and monolingual STS for languages without this evaluation data. We also present the results of several state-of-the-art methods on these data which can be used as a baseline for further research. We believe that this article will not only extend the current STS research to other languages, but will also encourage competition on this new evaluation data.</abstract>
    <identifier type="citekey">hercig-kral-2021-evaluation</identifier>
    <location>
        <url>https://aclanthology.org/2021.ranlp-1.59</url>
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        <date>2021-09</date>
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            <start>524</start>
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%0 Conference Proceedings
%T Evaluation Datasets for Cross-lingual Semantic Textual Similarity
%A Hercig, Tomáš
%A Kral, Pavel
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F hercig-kral-2021-evaluation
%X Semantic textual similarity (STS) systems estimate the degree of the meaning similarity between two sentences. Cross-lingual STS systems estimate the degree of the meaning similarity between two sentences, each in a different language. State-of-the-art algorithms usually employ a strongly supervised, resource-rich approach difficult to use for poorly-resourced languages. However, any approach needs to have evaluation data to confirm the results. In order to simplify the evaluation process for poorly-resourced languages (in terms of STS evaluation datasets), we present new datasets for cross-lingual and monolingual STS for languages without this evaluation data. We also present the results of several state-of-the-art methods on these data which can be used as a baseline for further research. We believe that this article will not only extend the current STS research to other languages, but will also encourage competition on this new evaluation data.
%U https://aclanthology.org/2021.ranlp-1.59
%P 524-529

Markdown (Informal)

[Evaluation Datasets for Cross-lingual Semantic Textual Similarity](https://aclanthology.org/2021.ranlp-1.59) (Hercig & Kral, RANLP 2021)

• Evaluation Datasets for Cross-lingual Semantic Textual Similarity (Hercig & Kral, RANLP 2021)

ACL

• Tomáš Hercig and Pavel Kral. 2021. Evaluation Datasets for Cross-lingual Semantic Textual Similarity. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 524–529, Held Online. INCOMA Ltd..