<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="swanson-etal-2019-building">
    <titleInfo>
        <title>Building a Production Model for Retrieval-Based Chatbots</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kyle</namePart>
        <namePart type="family">Swanson</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lili</namePart>
        <namePart type="family">Yu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Christopher</namePart>
        <namePart type="family">Fox</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jeremy</namePart>
        <namePart type="family">Wohlwend</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tao</namePart>
        <namePart type="family">Lei</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the First Workshop on NLP for Conversational AI</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tania</namePart>
            <namePart type="family">Bedrax-Weiss</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dilek</namePart>
            <namePart type="family">Hakkani-Tur</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anuj</namePart>
            <namePart type="family">Kumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mike</namePart>
            <namePart type="family">Lewis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Thang-Minh</namePart>
            <namePart type="family">Luong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pei-Hao</namePart>
            <namePart type="family">Su</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tsung-Hsien</namePart>
            <namePart type="family">Wen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Response suggestion is an important task for building human-computer conversation systems. Recent approaches to conversation modeling have introduced new model architectures with impressive results, but relatively little attention has been paid to whether these models would be practical in a production setting. In this paper, we describe the unique challenges of building a production retrieval-based conversation system, which selects outputs from a whitelist of candidate responses. To address these challenges, we propose a dual encoder architecture which performs rapid inference and scales well with the size of the whitelist. We also introduce and compare two methods for generating whitelists, and we carry out a comprehensive analysis of the model and whitelists. Experimental results on a large, proprietary help desk chat dataset, including both offline metrics and a human evaluation, indicate production-quality performance and illustrate key lessons about conversation modeling in practice.</abstract>
    <identifier type="citekey">swanson-etal-2019-building</identifier>
    <identifier type="doi">10.18653/v1/W19-4104</identifier>
    <location>
        <url>https://aclanthology.org/W19-4104</url>
    </location>
    <part>
        <date>2019-08</date>
        <extent unit="page">
            <start>32</start>
            <end>41</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Building a Production Model for Retrieval-Based Chatbots
%A Swanson, Kyle
%A Yu, Lili
%A Fox, Christopher
%A Wohlwend, Jeremy
%A Lei, Tao
%Y Chen, Yun-Nung
%Y Bedrax-Weiss, Tania
%Y Hakkani-Tur, Dilek
%Y Kumar, Anuj
%Y Lewis, Mike
%Y Luong, Thang-Minh
%Y Su, Pei-Hao
%Y Wen, Tsung-Hsien
%S Proceedings of the First Workshop on NLP for Conversational AI
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F swanson-etal-2019-building
%X Response suggestion is an important task for building human-computer conversation systems. Recent approaches to conversation modeling have introduced new model architectures with impressive results, but relatively little attention has been paid to whether these models would be practical in a production setting. In this paper, we describe the unique challenges of building a production retrieval-based conversation system, which selects outputs from a whitelist of candidate responses. To address these challenges, we propose a dual encoder architecture which performs rapid inference and scales well with the size of the whitelist. We also introduce and compare two methods for generating whitelists, and we carry out a comprehensive analysis of the model and whitelists. Experimental results on a large, proprietary help desk chat dataset, including both offline metrics and a human evaluation, indicate production-quality performance and illustrate key lessons about conversation modeling in practice.
%R 10.18653/v1/W19-4104
%U https://aclanthology.org/W19-4104
%U https://doi.org/10.18653/v1/W19-4104
%P 32-41

Markdown (Informal)

[Building a Production Model for Retrieval-Based Chatbots](https://aclanthology.org/W19-4104) (Swanson et al., ACL 2019)

• Building a Production Model for Retrieval-Based Chatbots (Swanson et al., ACL 2019)

ACL

• Kyle Swanson, Lili Yu, Christopher Fox, Jeremy Wohlwend, and Tao Lei. 2019. Building a Production Model for Retrieval-Based Chatbots. In Proceedings of the First Workshop on NLP for Conversational AI, pages 32–41, Florence, Italy. Association for Computational Linguistics.