<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bingel-sogaard-2017-identifying">
    <titleInfo>
        <title>Identifying beneficial task relations for multi-task learning in deep neural networks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Joachim</namePart>
        <namePart type="family">Bingel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anders</namePart>
        <namePart type="family">Søgaard</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Phil</namePart>
            <namePart type="family">Blunsom</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alexander</namePart>
            <namePart type="family">Koller</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Valencia, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP tasks, mixed results have been reported, and little is known about the conditions under which MTL leads to gains in NLP. This paper sheds light on the specific task relations that can lead to gains from MTL models over single-task setups.</abstract>
    <identifier type="citekey">bingel-sogaard-2017-identifying</identifier>
    <location>
        <url>https://aclanthology.org/E17-2026</url>
    </location>
    <part>
        <date>2017-04</date>
        <extent unit="page">
            <start>164</start>
            <end>169</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Identifying beneficial task relations for multi-task learning in deep neural networks
%A Bingel, Joachim
%A Søgaard, Anders
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F bingel-sogaard-2017-identifying
%X Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP tasks, mixed results have been reported, and little is known about the conditions under which MTL leads to gains in NLP. This paper sheds light on the specific task relations that can lead to gains from MTL models over single-task setups.
%U https://aclanthology.org/E17-2026
%P 164-169

Markdown (Informal)

[Identifying beneficial task relations for multi-task learning in deep neural networks](https://aclanthology.org/E17-2026) (Bingel & Søgaard, EACL 2017)

• Identifying beneficial task relations for multi-task learning in deep neural networks (Bingel & Søgaard, EACL 2017)

ACL

• Joachim Bingel and Anders Søgaard. 2017. Identifying beneficial task relations for multi-task learning in deep neural networks. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 164–169, Valencia, Spain. Association for Computational Linguistics.