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        <title>An Improved Neural Network Model for Joint POS Tagging and Dependency Parsing</title>
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        <namePart type="given">Dat</namePart>
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    <name type="personal">
        <namePart type="given">Karin</namePart>
        <namePart type="family">Verspoor</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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        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Zeman</namePart>
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            <namePart type="given">Jan</namePart>
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    <abstract>We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 “big” Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakova, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: https://github.com/datquocnguyen/jPTDP</abstract>
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    <identifier type="doi">10.18653/v1/K18-2008</identifier>
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%0 Conference Proceedings
%T An Improved Neural Network Model for Joint POS Tagging and Dependency Parsing
%A Nguyen, Dat Quoc
%A Verspoor, Karin
%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 nguyen-verspoor-2018-improved
%X We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 “big” Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakova, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: https://github.com/datquocnguyen/jPTDP
%R 10.18653/v1/K18-2008
%U https://aclanthology.org/K18-2008
%U https://doi.org/10.18653/v1/K18-2008
%P 81-91

Markdown (Informal)

[An Improved Neural Network Model for Joint POS Tagging and Dependency Parsing](https://aclanthology.org/K18-2008) (Nguyen & Verspoor, CoNLL 2018)

• An Improved Neural Network Model for Joint POS Tagging and Dependency Parsing (Nguyen & Verspoor, CoNLL 2018)

ACL

• Dat Quoc Nguyen and Karin Verspoor. 2018. An Improved Neural Network Model for Joint POS Tagging and Dependency Parsing. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 81–91, Brussels, Belgium. Association for Computational Linguistics.