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    <titleInfo>
        <title>Cross-Lingual Content Scoring</title>
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    <name type="personal">
        <namePart type="given">Andrea</namePart>
        <namePart type="family">Horbach</namePart>
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            <title>Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
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        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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        <name type="personal">
            <namePart type="given">Jill</namePart>
            <namePart type="family">Burstein</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Claudia</namePart>
            <namePart type="family">Leacock</namePart>
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        <name type="personal">
            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
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    <abstract>We investigate the feasibility of cross-lingual content scoring, a scenario where training and test data in an automatic scoring task are from two different languages. Cross-lingual scoring can contribute to educational equality by allowing answers in multiple languages. Training a model in one language and applying it to another language might also help to overcome data sparsity issues by re-using trained models from other languages. As there is no suitable dataset available for this new task, we create a comparable bi-lingual corpus by extending the English ASAP dataset with German answers. Our experiments with cross-lingual scoring based on machine-translating either training or test data show a considerable drop in scoring quality.</abstract>
    <identifier type="citekey">horbach-etal-2018-cross</identifier>
    <identifier type="doi">10.18653/v1/W18-0550</identifier>
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        <date>2018-06</date>
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            <start>410</start>
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%0 Conference Proceedings
%T Cross-Lingual Content Scoring
%A Horbach, Andrea
%A Stennmanns, Sebastian
%A Zesch, Torsten
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F horbach-etal-2018-cross
%X We investigate the feasibility of cross-lingual content scoring, a scenario where training and test data in an automatic scoring task are from two different languages. Cross-lingual scoring can contribute to educational equality by allowing answers in multiple languages. Training a model in one language and applying it to another language might also help to overcome data sparsity issues by re-using trained models from other languages. As there is no suitable dataset available for this new task, we create a comparable bi-lingual corpus by extending the English ASAP dataset with German answers. Our experiments with cross-lingual scoring based on machine-translating either training or test data show a considerable drop in scoring quality.
%R 10.18653/v1/W18-0550
%U https://aclanthology.org/W18-0550
%U https://doi.org/10.18653/v1/W18-0550
%P 410-419

Markdown (Informal)

[Cross-Lingual Content Scoring](https://aclanthology.org/W18-0550) (Horbach et al., BEA 2018)

• Cross-Lingual Content Scoring (Horbach et al., BEA 2018)

ACL

• Andrea Horbach, Sebastian Stennmanns, and Torsten Zesch. 2018. Cross-Lingual Content Scoring. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 410–419, New Orleans, Louisiana. Association for Computational Linguistics.