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        <title>SParse: Koç University Graph-Based Parsing System for the CoNLL 2018 Shared Task</title>
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        <namePart type="given">Berkay</namePart>
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    <name type="personal">
        <namePart type="given">Deniz</namePart>
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            <title>Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies</title>
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            <namePart type="given">Daniel</namePart>
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            <namePart type="given">Jan</namePart>
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    <abstract>We present SParse, our Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et al., 2018). Our model extends the state-of-the-art biaffine parser (Dozat and Manning, 2016) with a structural meta-learning module, SMeta, that combines local and global label predictions. Our parser has been trained and run on Universal Dependencies datasets (Nivre et al., 2016, 2018) and has 87.48% LAS, 78.63% MLAS, 78.69% BLEX and 81.76% CLAS (Nivre and Fang, 2017) score on the Italian-ISDT dataset and has 72.78% LAS, 59.10% MLAS, 61.38% BLEX and 61.72% CLAS score on the Japanese-GSD dataset in our official submission. All other corpora are evaluated after the submission deadline, for whom we present our unofficial test results.</abstract>
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    <identifier type="doi">10.18653/v1/K18-2022</identifier>
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%0 Conference Proceedings
%T SParse: Koç University Graph-Based Parsing System for the CoNLL 2018 Shared Task
%A Önder, Berkay
%A Gümeli, Can
%A Yuret, Deniz
%Y Zeman, Daniel
%Y Hajič, Jan
%S Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F onder-etal-2018-sparse
%X We present SParse, our Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et al., 2018). Our model extends the state-of-the-art biaffine parser (Dozat and Manning, 2016) with a structural meta-learning module, SMeta, that combines local and global label predictions. Our parser has been trained and run on Universal Dependencies datasets (Nivre et al., 2016, 2018) and has 87.48% LAS, 78.63% MLAS, 78.69% BLEX and 81.76% CLAS (Nivre and Fang, 2017) score on the Italian-ISDT dataset and has 72.78% LAS, 59.10% MLAS, 61.38% BLEX and 61.72% CLAS score on the Japanese-GSD dataset in our official submission. All other corpora are evaluated after the submission deadline, for whom we present our unofficial test results.
%R 10.18653/v1/K18-2022
%U https://aclanthology.org/K18-2022
%U https://doi.org/10.18653/v1/K18-2022
%P 216-222

Markdown (Informal)

[SParse: Koç University Graph-Based Parsing System for the CoNLL 2018 Shared Task](https://aclanthology.org/K18-2022) (Önder et al., CoNLL 2018)

• SParse: Koç University Graph-Based Parsing System for the CoNLL 2018 Shared Task (Önder et al., CoNLL 2018)

ACL

• Berkay Önder, Can Gümeli, and Deniz Yuret. 2018. SParse: Koç University Graph-Based Parsing System for the CoNLL 2018 Shared Task. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 216–222, Brussels, Belgium. Association for Computational Linguistics.