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        <title>A Transition-based System for Universal Dependency Parsing</title>
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    <name type="personal">
        <namePart type="given">Hao</namePart>
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        <namePart type="family">Zhao</namePart>
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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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            <namePart type="given">Jan</namePart>
            <namePart type="family">Hajič</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>This paper describes the system for our participation in the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. In this work, we design a system based on UDPipe1 for universal dependency parsing, where multilingual transition-based models are trained for different treebanks. Our system directly takes raw texts as input, performing several intermediate steps like tokenizing and tagging, and finally generates the corresponding dependency trees. For the special surprise languages for this task, we adopt a delexicalized strategy and predict basing on transfer learning from other related languages. In the final evaluation of the shared task, our system achieves a result of 66.53% in macro-averaged LAS F1-score.</abstract>
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    <identifier type="doi">10.18653/v1/K17-3020</identifier>
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%0 Conference Proceedings
%T A Transition-based System for Universal Dependency Parsing
%A Wang, Hao
%A Zhao, Hai
%A Zhang, Zhisong
%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 wang-etal-2017-transition-based
%X This paper describes the system for our participation in the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. In this work, we design a system based on UDPipe1 for universal dependency parsing, where multilingual transition-based models are trained for different treebanks. Our system directly takes raw texts as input, performing several intermediate steps like tokenizing and tagging, and finally generates the corresponding dependency trees. For the special surprise languages for this task, we adopt a delexicalized strategy and predict basing on transfer learning from other related languages. In the final evaluation of the shared task, our system achieves a result of 66.53% in macro-averaged LAS F1-score.
%R 10.18653/v1/K17-3020
%U https://aclanthology.org/K17-3020
%U https://doi.org/10.18653/v1/K17-3020
%P 191-197

Markdown (Informal)

[A Transition-based System for Universal Dependency Parsing](https://aclanthology.org/K17-3020) (Wang et al., CoNLL 2017)

• A Transition-based System for Universal Dependency Parsing (Wang et al., CoNLL 2017)

ACL

• Hao Wang, Hai Zhao, and Zhisong Zhang. 2017. A Transition-based System for Universal Dependency Parsing. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 191–197, Vancouver, Canada. Association for Computational Linguistics.