<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="golchha-etal-2019-courteously">
    <titleInfo>
        <title>Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hitesh</namePart>
        <namePart type="family">Golchha</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mauajama</namePart>
        <namePart type="family">Firdaus</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Asif</namePart>
        <namePart type="family">Ekbal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Pushpak</namePart>
        <namePart type="family">Bhattacharyya</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Jill</namePart>
            <namePart type="family">Burstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christy</namePart>
            <namePart type="family">Doran</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Thamar</namePart>
            <namePart type="family">Solorio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Minneapolis, Minnesota</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we propose an effective deep learning framework for inducing courteous behavior in customer care responses. The interaction between a customer and the customer care representative contributes substantially to the overall customer experience. Thus it is imperative for customer care agents and chatbots engaging with humans to be personal, cordial and emphatic to ensure customer satisfaction and retention. Our system aims at automatically transforming neutral customer care responses into courteous replies. Along with stylistic transfer (of courtesy), our system ensures that responses are coherent with the conversation history, and generates courteous expressions consistent with the emotional state of the customer. Our technique is based on a reinforced pointer-generator model for the sequence to sequence task. The model is also conditioned on a hierarchically encoded and emotionally aware conversational context. We use real interactions on Twitter between customer care professionals and aggrieved customers to create a large conversational dataset having both forms of agent responses: ‘generic’ and ‘courteous’. We perform quantitative and qualitative analyses on established and task-specific metrics, both automatic and human evaluation based. Our evaluation shows that the proposed models can generate emotionally-appropriate courteous expressions while preserving the content. Experimental results also prove that our proposed approach performs better than the baseline models.</abstract>
    <identifier type="citekey">golchha-etal-2019-courteously</identifier>
    <identifier type="doi">10.18653/v1/N19-1091</identifier>
    <location>
        <url>https://aclanthology.org/N19-1091</url>
    </location>
    <part>
        <date>2019-06</date>
        <extent unit="page">
            <start>851</start>
            <end>860</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network
%A Golchha, Hitesh
%A Firdaus, Mauajama
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F golchha-etal-2019-courteously
%X In this paper, we propose an effective deep learning framework for inducing courteous behavior in customer care responses. The interaction between a customer and the customer care representative contributes substantially to the overall customer experience. Thus it is imperative for customer care agents and chatbots engaging with humans to be personal, cordial and emphatic to ensure customer satisfaction and retention. Our system aims at automatically transforming neutral customer care responses into courteous replies. Along with stylistic transfer (of courtesy), our system ensures that responses are coherent with the conversation history, and generates courteous expressions consistent with the emotional state of the customer. Our technique is based on a reinforced pointer-generator model for the sequence to sequence task. The model is also conditioned on a hierarchically encoded and emotionally aware conversational context. We use real interactions on Twitter between customer care professionals and aggrieved customers to create a large conversational dataset having both forms of agent responses: ‘generic’ and ‘courteous’. We perform quantitative and qualitative analyses on established and task-specific metrics, both automatic and human evaluation based. Our evaluation shows that the proposed models can generate emotionally-appropriate courteous expressions while preserving the content. Experimental results also prove that our proposed approach performs better than the baseline models.
%R 10.18653/v1/N19-1091
%U https://aclanthology.org/N19-1091
%U https://doi.org/10.18653/v1/N19-1091
%P 851-860

Markdown (Informal)

[Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network](https://aclanthology.org/N19-1091) (Golchha et al., NAACL 2019)

• Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network (Golchha et al., NAACL 2019)

ACL

• Hitesh Golchha, Mauajama Firdaus, Asif Ekbal, and Pushpak Bhattacharyya. 2019. Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 851–860, Minneapolis, Minnesota. Association for Computational Linguistics.