<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="augenstein-etal-2018-multi">
    <titleInfo>
        <title>Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Isabelle</namePart>
        <namePart type="family">Augenstein</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sebastian</namePart>
        <namePart type="family">Ruder</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>2018-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marilyn</namePart>
            <namePart type="family">Walker</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Heng</namePart>
            <namePart type="family">Ji</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Amanda</namePart>
            <namePart type="family">Stent</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">New Orleans, Louisiana</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state of the art for aspect-based and topic-based sentiment analysis.</abstract>
    <identifier type="citekey">augenstein-etal-2018-multi</identifier>
    <identifier type="doi">10.18653/v1/N18-1172</identifier>
    <location>
        <url>https://aclanthology.org/N18-1172</url>
    </location>
    <part>
        <date>2018-06</date>
        <extent unit="page">
            <start>1896</start>
            <end>1906</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces
%A Augenstein, Isabelle
%A Ruder, Sebastian
%A Søgaard, Anders
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F augenstein-etal-2018-multi
%X We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state of the art for aspect-based and topic-based sentiment analysis.
%R 10.18653/v1/N18-1172
%U https://aclanthology.org/N18-1172
%U https://doi.org/10.18653/v1/N18-1172
%P 1896-1906

Markdown (Informal)

[Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces](https://aclanthology.org/N18-1172) (Augenstein et al., NAACL 2018)

• Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces (Augenstein et al., NAACL 2018)

ACL

• Isabelle Augenstein, Sebastian Ruder, and Anders Søgaard. 2018. Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1896–1906, New Orleans, Louisiana. Association for Computational Linguistics.