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        <title>Some Languages Seem Easier to Parse Because Their Treebanks Leak</title>
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        <namePart type="given">Anders</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
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        <name type="personal">
            <namePart type="given">Yulan</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Cross-language differences in (universal) dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences. We point at a factor not previously discussed: If we abstract away from words and dependency labels, how many graphs in the test data were seen in the training data? We compute graph isomorphisms, and show that, treebank size aside, overlap between training and test graphs explain more of the observed variation than standard explanations such as the above.</abstract>
    <identifier type="citekey">sogaard-2020-languages</identifier>
    <identifier type="doi">10.18653/v1/2020.emnlp-main.220</identifier>
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%0 Conference Proceedings
%T Some Languages Seem Easier to Parse Because Their Treebanks Leak
%A Søgaard, Anders
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sogaard-2020-languages
%X Cross-language differences in (universal) dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences. We point at a factor not previously discussed: If we abstract away from words and dependency labels, how many graphs in the test data were seen in the training data? We compute graph isomorphisms, and show that, treebank size aside, overlap between training and test graphs explain more of the observed variation than standard explanations such as the above.
%R 10.18653/v1/2020.emnlp-main.220
%U https://aclanthology.org/2020.emnlp-main.220
%U https://doi.org/10.18653/v1/2020.emnlp-main.220
%P 2765-2770

Markdown (Informal)

[Some Languages Seem Easier to Parse Because Their Treebanks Leak](https://aclanthology.org/2020.emnlp-main.220) (Søgaard, EMNLP 2020)

• Some Languages Seem Easier to Parse Because Their Treebanks Leak (Søgaard, EMNLP 2020)

ACL

• Anders Søgaard. 2020. Some Languages Seem Easier to Parse Because Their Treebanks Leak. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2765–2770, Online. Association for Computational Linguistics.