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        <title>A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations</title>
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        <namePart type="given">KyungTae</namePart>
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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>In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Task dealing with “Multilingual Parsing from Raw Text to Universal Dependencies”. Our parser extends the monolingual BIST-parser as a multi-source multilingual trainable parser. Thanks to multilingual word embeddings and one hot encodings for languages, our system can use both monolingual and multi-source training. We trained 69 monolingual language models and 13 multilingual models for the shared task. Our multilingual approach making use of different resources yield better results than the monolingual approach for 11 languages. Our system ranked 5 th and achieved 70.93 overall LAS score over the 81 test corpora (macro-averaged LAS F1 score).</abstract>
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    <identifier type="doi">10.18653/v1/K17-3006</identifier>
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%0 Conference Proceedings
%T A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations
%A Lim, KyungTae
%A Poibeau, Thierry
%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 lim-poibeau-2017-system
%X In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Task dealing with “Multilingual Parsing from Raw Text to Universal Dependencies”. Our parser extends the monolingual BIST-parser as a multi-source multilingual trainable parser. Thanks to multilingual word embeddings and one hot encodings for languages, our system can use both monolingual and multi-source training. We trained 69 monolingual language models and 13 multilingual models for the shared task. Our multilingual approach making use of different resources yield better results than the monolingual approach for 11 languages. Our system ranked 5 th and achieved 70.93 overall LAS score over the 81 test corpora (macro-averaged LAS F1 score).
%R 10.18653/v1/K17-3006
%U https://aclanthology.org/K17-3006
%U https://doi.org/10.18653/v1/K17-3006
%P 63-70

Markdown (Informal)

[A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations](https://aclanthology.org/K17-3006) (Lim & Poibeau, CoNLL 2017)

• A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations (Lim & Poibeau, CoNLL 2017)

ACL

• KyungTae Lim and Thierry Poibeau. 2017. A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 63–70, Vancouver, Canada. Association for Computational Linguistics.