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        <title>Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement</title>
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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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    <abstract>This paper describes the system of our team Phoenix for participating CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Given the annotated gold standard data in CoNLL-U format, we train the tokenizer, tagger and parser separately for each treebank based on an open source pipeline tool UDPipe. Our system reads the plain texts for input, performs the pre-processing steps (tokenization, lemmas, morphology) and finally outputs the syntactic dependencies. For the low-resource languages with no training data, we use cross-lingual techniques to build models with some close languages instead. In the official evaluation, our system achieves the macro-averaged scores of 65.61%, 52.26%, 55.71% for LAS, MLAS and BLEX respectively.</abstract>
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    <identifier type="doi">10.18653/v1/K18-2007</identifier>
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%0 Conference Proceedings
%T Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement
%A Wu, Yingting
%A Zhao, Hai
%A Tong, Jia-Jun
%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 wu-etal-2018-multilingual
%X This paper describes the system of our team Phoenix for participating CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Given the annotated gold standard data in CoNLL-U format, we train the tokenizer, tagger and parser separately for each treebank based on an open source pipeline tool UDPipe. Our system reads the plain texts for input, performs the pre-processing steps (tokenization, lemmas, morphology) and finally outputs the syntactic dependencies. For the low-resource languages with no training data, we use cross-lingual techniques to build models with some close languages instead. In the official evaluation, our system achieves the macro-averaged scores of 65.61%, 52.26%, 55.71% for LAS, MLAS and BLEX respectively.
%R 10.18653/v1/K18-2007
%U https://aclanthology.org/K18-2007
%U https://doi.org/10.18653/v1/K18-2007
%P 74-80

Markdown (Informal)

[Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement](https://aclanthology.org/K18-2007) (Wu et al., CoNLL 2018)

• Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement (Wu et al., CoNLL 2018)

ACL

• Yingting Wu, Hai Zhao, and Jia-Jun Tong. 2018. Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 74–80, Brussels, Belgium. Association for Computational Linguistics.