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        <title>On Language Spaces, Scales and Cross-Lingual Transfer of UD Parsers</title>
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    <name type="personal">
        <namePart type="given">Tanja</namePart>
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        <namePart type="given">Ximena</namePart>
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        <namePart type="given">Max</namePart>
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    <name type="personal">
        <namePart type="given">Olga</namePart>
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        <namePart type="given">Barbara</namePart>
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            <namePart type="given">Antske</namePart>
            <namePart type="family">Fokkens</namePart>
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        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
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    <abstract>Cross-lingual transfer of parsing models has been shown to work well for several closely-related languages, but predicting the success in other cases remains hard. Our study is a comprehensive analysis of the impact of linguistic distance on the transfer of UD parsers. As an alternative to syntactic typological distances extracted from URIEL, we propose three text-based feature spaces and show that they can be more precise predictors, especially on a more local scale, when only shorter distances are taken into account. Our analyses also reveal that the good coverage in typological databases is not among the factors that explain good transfer.</abstract>
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    <identifier type="doi">10.18653/v1/2022.conll-1.18</identifier>
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        <date>2022-12</date>
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%0 Conference Proceedings
%T On Language Spaces, Scales and Cross-Lingual Transfer of UD Parsers
%A Samardžić, Tanja
%A Gutierrez-Vasques, Ximena
%A van der Goot, Rob
%A Müller-Eberstein, Max
%A Pelloni, Olga
%A Plank, Barbara
%Y Fokkens, Antske
%Y Srikumar, Vivek
%S Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F samardzic-etal-2022-language
%X Cross-lingual transfer of parsing models has been shown to work well for several closely-related languages, but predicting the success in other cases remains hard. Our study is a comprehensive analysis of the impact of linguistic distance on the transfer of UD parsers. As an alternative to syntactic typological distances extracted from URIEL, we propose three text-based feature spaces and show that they can be more precise predictors, especially on a more local scale, when only shorter distances are taken into account. Our analyses also reveal that the good coverage in typological databases is not among the factors that explain good transfer.
%R 10.18653/v1/2022.conll-1.18
%U https://aclanthology.org/2022.conll-1.18
%U https://doi.org/10.18653/v1/2022.conll-1.18
%P 266-281

Markdown (Informal)

[On Language Spaces, Scales and Cross-Lingual Transfer of UD Parsers](https://aclanthology.org/2022.conll-1.18) (Samardžić et al., CoNLL 2022)

• On Language Spaces, Scales and Cross-Lingual Transfer of UD Parsers (Samardžić et al., CoNLL 2022)

ACL

• Tanja Samardžić, Ximena Gutierrez-Vasques, Rob van der Goot, Max Müller-Eberstein, Olga Pelloni, and Barbara Plank. 2022. On Language Spaces, Scales and Cross-Lingual Transfer of UD Parsers. In Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL), pages 266–281, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.