<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nguyen-etal-2017-novel">
    <titleInfo>
        <title>A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Dat</namePart>
        <namePart type="given">Quoc</namePart>
        <namePart type="family">Nguyen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mark</namePart>
        <namePart type="family">Dras</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mark</namePart>
        <namePart type="family">Johnson</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Hajič</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Zeman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem. Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art. Our code is open-source and available together with pre-trained models at: https://github.com/datquocnguyen/jPTDP</abstract>
    <identifier type="citekey">nguyen-etal-2017-novel</identifier>
    <identifier type="doi">10.18653/v1/K17-3014</identifier>
    <location>
        <url>https://aclanthology.org/K17-3014</url>
    </location>
    <part>
        <date>2017-08</date>
        <extent unit="page">
            <start>134</start>
            <end>142</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
%A Nguyen, Dat Quoc
%A Dras, Mark
%A Johnson, Mark
%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 nguyen-etal-2017-novel
%X We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem. Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art. Our code is open-source and available together with pre-trained models at: https://github.com/datquocnguyen/jPTDP
%R 10.18653/v1/K17-3014
%U https://aclanthology.org/K17-3014
%U https://doi.org/10.18653/v1/K17-3014
%P 134-142

Markdown (Informal)

[A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing](https://aclanthology.org/K17-3014) (Nguyen et al., CoNLL 2017)

• A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing (Nguyen et al., CoNLL 2017)

ACL

• Dat Quoc Nguyen, Mark Dras, and Mark Johnson. 2017. A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 134–142, Vancouver, Canada. Association for Computational Linguistics.