Bleaching Text: Abstract Features for Cross-lingual Gender Prediction

Rob van der Goot, Nikola Ljubesi, Ian Matroos, Malvina Nissim, Barbara Plank

    Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

    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.
    OriginalsprogEngelsk
    TitelProceedings of the 56th Annual Meeting of the Association for Computational Linguistics
    Antal sider7
    UdgivelsesstedMelbourne
    ForlagAssociation for Computational Linguistics
    Publikationsdato2018
    StatusUdgivet - 2018

    Emneord

    • Gender prediction
    • Cross-lingual embeddings
    • Text bleaching
    • Human prediction
    • Lexical features

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