<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="herzig-etal-2017-neural">
    <titleInfo>
        <title>Neural Response Generation for Customer Service based on Personality Traits</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jonathan</namePart>
        <namePart type="family">Herzig</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Michal</namePart>
        <namePart type="family">Shmueli-Scheuer</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tommy</namePart>
        <namePart type="family">Sandbank</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">David</namePart>
        <namePart type="family">Konopnicki</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 10th International Conference on Natural Language Generation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Jose</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Alonso</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alberto</namePart>
            <namePart type="family">Bugarín</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ehud</namePart>
            <namePart type="family">Reiter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Santiago de Compostela, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We present a neural response generation model that generates responses conditioned on a target personality. The model learns high level features based on the target personality, and uses them to update its hidden state. Our model achieves performance improvements in both perplexity and BLEU scores over a baseline sequence-to-sequence model, and is validated by human judges.</abstract>
    <identifier type="citekey">herzig-etal-2017-neural</identifier>
    <identifier type="doi">10.18653/v1/W17-3541</identifier>
    <location>
        <url>https://aclanthology.org/W17-3541</url>
    </location>
    <part>
        <date>2017-09</date>
        <extent unit="page">
            <start>252</start>
            <end>256</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural Response Generation for Customer Service based on Personality Traits
%A Herzig, Jonathan
%A Shmueli-Scheuer, Michal
%A Sandbank, Tommy
%A Konopnicki, David
%Y Alonso, Jose M.
%Y Bugarín, Alberto
%Y Reiter, Ehud
%S Proceedings of the 10th International Conference on Natural Language Generation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Santiago de Compostela, Spain
%F herzig-etal-2017-neural
%X We present a neural response generation model that generates responses conditioned on a target personality. The model learns high level features based on the target personality, and uses them to update its hidden state. Our model achieves performance improvements in both perplexity and BLEU scores over a baseline sequence-to-sequence model, and is validated by human judges.
%R 10.18653/v1/W17-3541
%U https://aclanthology.org/W17-3541
%U https://doi.org/10.18653/v1/W17-3541
%P 252-256

Markdown (Informal)

[Neural Response Generation for Customer Service based on Personality Traits](https://aclanthology.org/W17-3541) (Herzig et al., INLG 2017)

• Neural Response Generation for Customer Service based on Personality Traits (Herzig et al., INLG 2017)

ACL

• Jonathan Herzig, Michal Shmueli-Scheuer, Tommy Sandbank, and David Konopnicki. 2017. Neural Response Generation for Customer Service based on Personality Traits. In Proceedings of the 10th International Conference on Natural Language Generation, pages 252–256, Santiago de Compostela, Spain. Association for Computational Linguistics.