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        <title>UParse: the Edinburgh system for the CoNLL 2017 UD shared task</title>
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    <name type="personal">
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            <title>Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies</title>
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        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Hajič</namePart>
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        <name type="personal">
            <namePart type="given">Dan</namePart>
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    <abstract>This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirectional LSTM to to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for low-resource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants, our system achieves rank 9th in the main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81 treebanks).</abstract>
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%0 Conference Proceedings
%T UParse: the Edinburgh system for the CoNLL 2017 UD shared task
%A Vania, Clara
%A Zhang, Xingxing
%A Lopez, Adam
%Y Hajič, Jan
%Y Zeman, Dan
%S Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F vania-etal-2017-uparse
%X This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirectional LSTM to to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for low-resource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants, our system achieves rank 9th in the main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81 treebanks).
%R 10.18653/v1/K17-3010
%U https://aclanthology.org/K17-3010
%U https://doi.org/10.18653/v1/K17-3010
%P 100-110

Markdown (Informal)

[UParse: the Edinburgh system for the CoNLL 2017 UD shared task](https://aclanthology.org/K17-3010) (Vania et al., CoNLL 2017)

• UParse: the Edinburgh system for the CoNLL 2017 UD shared task (Vania et al., CoNLL 2017)

ACL

• Clara Vania, Xingxing Zhang, and Adam Lopez. 2017. UParse: the Edinburgh system for the CoNLL 2017 UD shared task. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 100–110, Vancouver, Canada. Association for Computational Linguistics.