<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="vlachos-etal-2017-imitation">
    <titleInfo>
        <title>Imitation learning for structured prediction in natural language processing</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Andreas</namePart>
        <namePart type="family">Vlachos</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gerasimos</namePart>
        <namePart type="family">Lampouras</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sebastian</namePart>
        <namePart type="family">Riedel</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: Tutorial Abstracts</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Alexandre</namePart>
            <namePart type="family">Klementiev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</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>Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e.g. autonomous flight from pilot demonstrations. Recently, algorithms for structured prediction were proposed under this paradigm and have been applied successfully to a number of tasks including syntactic dependency parsing, information extraction, coreference resolution, dynamic feature selection, semantic parsing and natural language generation. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Our aim in this tutorial is to have a unified presentation of the various imitation algorithms for structure prediction, and show how they can be applied to a variety of NLP tasks.All material associated with the tutorial will be made available through https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017/.</abstract>
    <identifier type="citekey">vlachos-etal-2017-imitation</identifier>
    <location>
        <url>https://aclanthology.org/E17-5003</url>
    </location>
    <part>
        <date>2017-04</date>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Imitation learning for structured prediction in natural language processing
%A Vlachos, Andreas
%A Lampouras, Gerasimos
%A Riedel, Sebastian
%Y Klementiev, Alexandre
%Y Specia, Lucia
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F vlachos-etal-2017-imitation
%X Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e.g. autonomous flight from pilot demonstrations. Recently, algorithms for structured prediction were proposed under this paradigm and have been applied successfully to a number of tasks including syntactic dependency parsing, information extraction, coreference resolution, dynamic feature selection, semantic parsing and natural language generation. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Our aim in this tutorial is to have a unified presentation of the various imitation algorithms for structure prediction, and show how they can be applied to a variety of NLP tasks.All material associated with the tutorial will be made available through https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017/.
%U https://aclanthology.org/E17-5003

Markdown (Informal)

[Imitation learning for structured prediction in natural language processing](https://aclanthology.org/E17-5003) (Vlachos et al., EACL 2017)

• Imitation learning for structured prediction in natural language processing (Vlachos et al., EACL 2017)

ACL

• Andreas Vlachos, Gerasimos Lampouras, and Sebastian Riedel. 2017. Imitation learning for structured prediction in natural language processing. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, Valencia, Spain. Association for Computational Linguistics.