<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="colas-etal-2020-efficient">
    <titleInfo>
        <title>Efficient Deployment of Conversational Natural Language Interfaces over Databases</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Anthony</namePart>
        <namePart type="family">Colas</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Trung</namePart>
        <namePart type="family">Bui</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Franck</namePart>
        <namePart type="family">Dernoncourt</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Moumita</namePart>
        <namePart type="family">Sinha</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Doo</namePart>
        <namePart type="given">Soon</namePart>
        <namePart type="family">Kim</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the First Workshop on Natural Language Interfaces</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ahmed</namePart>
            <namePart type="given">Hassan</namePart>
            <namePart type="family">Awadallah</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yu</namePart>
            <namePart type="family">Su</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Huan</namePart>
            <namePart type="family">Sun</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Scott</namePart>
            <namePart type="given">Wen-tau</namePart>
            <namePart type="family">Yih</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Many users communicate with chatbots and AI assistants in order to help them with various tasks. A key component of the assistant is the ability to understand and answer a user’s natural language questions for question-answering (QA). Because data can be usually stored in a structured manner, an essential step involves turning a natural language question into its corresponding query language. However, in order to train most natural language-to-query-language state-of-the-art models, a large amount of training data is needed first. In most domains, this data is not available and collecting such datasets for various domains can be tedious and time-consuming. In this work, we propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models. Our system allows one to generate conversational multi-term data, where multiple turns define a dialogue session, enabling one to better utilize chatbot interfaces. We train two current state-of-the-art NL-to-QL models, on both an SQL and SPARQL-based datasets in order to showcase the adaptability and efficacy of our created data.</abstract>
    <identifier type="citekey">colas-etal-2020-efficient</identifier>
    <identifier type="doi">10.18653/v1/2020.nli-1.4</identifier>
    <location>
        <url>https://aclanthology.org/2020.nli-1.4</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>27</start>
            <end>36</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Efficient Deployment of Conversational Natural Language Interfaces over Databases
%A Colas, Anthony
%A Bui, Trung
%A Dernoncourt, Franck
%A Sinha, Moumita
%A Kim, Doo Soon
%Y Awadallah, Ahmed Hassan
%Y Su, Yu
%Y Sun, Huan
%Y Yih, Scott Wen-tau
%S Proceedings of the First Workshop on Natural Language Interfaces
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F colas-etal-2020-efficient
%X Many users communicate with chatbots and AI assistants in order to help them with various tasks. A key component of the assistant is the ability to understand and answer a user’s natural language questions for question-answering (QA). Because data can be usually stored in a structured manner, an essential step involves turning a natural language question into its corresponding query language. However, in order to train most natural language-to-query-language state-of-the-art models, a large amount of training data is needed first. In most domains, this data is not available and collecting such datasets for various domains can be tedious and time-consuming. In this work, we propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models. Our system allows one to generate conversational multi-term data, where multiple turns define a dialogue session, enabling one to better utilize chatbot interfaces. We train two current state-of-the-art NL-to-QL models, on both an SQL and SPARQL-based datasets in order to showcase the adaptability and efficacy of our created data.
%R 10.18653/v1/2020.nli-1.4
%U https://aclanthology.org/2020.nli-1.4
%U https://doi.org/10.18653/v1/2020.nli-1.4
%P 27-36

Markdown (Informal)

[Efficient Deployment of Conversational Natural Language Interfaces over Databases](https://aclanthology.org/2020.nli-1.4) (Colas et al., NLI 2020)

• Efficient Deployment of Conversational Natural Language Interfaces over Databases (Colas et al., NLI 2020)

ACL

• Anthony Colas, Trung Bui, Franck Dernoncourt, Moumita Sinha, and Doo Soon Kim. 2020. Efficient Deployment of Conversational Natural Language Interfaces over Databases. In Proceedings of the First Workshop on Natural Language Interfaces, pages 27–36, Online. Association for Computational Linguistics.