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        <title>Bleaching Text: Abstract Features for Cross-lingual Gender Prediction</title>
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    <name type="personal">
        <namePart type="given">Rob</namePart>
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    <name type="personal">
        <namePart type="given">Nikola</namePart>
        <namePart type="family">Ljubešić</namePart>
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    <name type="personal">
        <namePart type="given">Ian</namePart>
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    <name type="personal">
        <namePart type="given">Malvina</namePart>
        <namePart type="family">Nissim</namePart>
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    <name type="personal">
        <namePart type="given">Barbara</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Gender prediction has typically focused on lexical and social network features, yielding good performance, but making systems highly language-, topic-, and platform dependent. Cross-lingual embeddings circumvent some of these limitations, but capture gender-specific style less. We propose an alternative: bleaching text, i.e., transforming lexical strings into more abstract features. This study provides evidence that such features allow for better transfer across languages. Moreover, we present a first study on the ability of humans to perform cross-lingual gender prediction. We find that human predictive power proves similar to that of our bleached models, and both perform better than lexical models.</abstract>
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    <identifier type="doi">10.18653/v1/P18-2061</identifier>
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%0 Conference Proceedings
%T Bleaching Text: Abstract Features for Cross-lingual Gender Prediction
%A van der Goot, Rob
%A Ljubešić, Nikola
%A Matroos, Ian
%A Nissim, Malvina
%A Plank, Barbara
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F van-der-goot-etal-2018-bleaching
%X Gender prediction has typically focused on lexical and social network features, yielding good performance, but making systems highly language-, topic-, and platform dependent. Cross-lingual embeddings circumvent some of these limitations, but capture gender-specific style less. We propose an alternative: bleaching text, i.e., transforming lexical strings into more abstract features. This study provides evidence that such features allow for better transfer across languages. Moreover, we present a first study on the ability of humans to perform cross-lingual gender prediction. We find that human predictive power proves similar to that of our bleached models, and both perform better than lexical models.
%R 10.18653/v1/P18-2061
%U https://aclanthology.org/P18-2061
%U https://doi.org/10.18653/v1/P18-2061
%P 383-389

Markdown (Informal)

[Bleaching Text: Abstract Features for Cross-lingual Gender Prediction](https://aclanthology.org/P18-2061) (van der Goot et al., ACL 2018)

• Bleaching Text: Abstract Features for Cross-lingual Gender Prediction (van der Goot et al., ACL 2018)

ACL

• Rob van der Goot, Nikola Ljubešić, Ian Matroos, Malvina Nissim, and Barbara Plank. 2018. Bleaching Text: Abstract Features for Cross-lingual Gender Prediction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 383–389, Melbourne, Australia. Association for Computational Linguistics.