<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="banerjee-etal-2018-dataset">
    <titleInfo>
        <title>A Dataset for Building Code-Mixed Goal Oriented Conversation Systems</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Suman</namePart>
        <namePart type="family">Banerjee</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nikita</namePart>
        <namePart type="family">Moghe</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Siddhartha</namePart>
        <namePart type="family">Arora</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mitesh</namePart>
        <namePart type="given">M</namePart>
        <namePart type="family">Khapra</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pierre</namePart>
            <namePart type="family">Isabelle</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Santa Fe, New Mexico, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>There is an increasing demand for goal-oriented conversation systems which can assist users in various day-to-day activities such as booking tickets, restaurant reservations, shopping, etc. Most of the existing datasets for building such conversation systems focus on monolingual conversations and there is hardly any work on multilingual and/or code-mixed conversations. Such datasets and systems thus do not cater to the multilingual regions of the world, such as India, where it is very common for people to speak more than one language and seamlessly switch between them resulting in code-mixed conversations. For example, a Hindi speaking user looking to book a restaurant would typically ask, “Kya tum is restaurant mein ek table book karne mein meri help karoge?” (“Can you help me in booking a table at this restaurant?”). To facilitate the development of such code-mixed conversation models, we build a goal-oriented dialog dataset containing code-mixed conversations. Specifically, we take the text from the DSTC2 restaurant reservation dataset and create code-mixed versions of it in Hindi-English, Bengali-English, Gujarati-English and Tamil-English. We also establish initial baselines on this dataset using existing state of the art models. This dataset along with our baseline implementations will be made publicly available for research purposes.</abstract>
    <identifier type="citekey">banerjee-etal-2018-dataset</identifier>
    <location>
        <url>https://aclanthology.org/C18-1319</url>
    </location>
    <part>
        <date>2018-08</date>
        <extent unit="page">
            <start>3766</start>
            <end>3780</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Dataset for Building Code-Mixed Goal Oriented Conversation Systems
%A Banerjee, Suman
%A Moghe, Nikita
%A Arora, Siddhartha
%A Khapra, Mitesh M.
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F banerjee-etal-2018-dataset
%X There is an increasing demand for goal-oriented conversation systems which can assist users in various day-to-day activities such as booking tickets, restaurant reservations, shopping, etc. Most of the existing datasets for building such conversation systems focus on monolingual conversations and there is hardly any work on multilingual and/or code-mixed conversations. Such datasets and systems thus do not cater to the multilingual regions of the world, such as India, where it is very common for people to speak more than one language and seamlessly switch between them resulting in code-mixed conversations. For example, a Hindi speaking user looking to book a restaurant would typically ask, “Kya tum is restaurant mein ek table book karne mein meri help karoge?” (“Can you help me in booking a table at this restaurant?”). To facilitate the development of such code-mixed conversation models, we build a goal-oriented dialog dataset containing code-mixed conversations. Specifically, we take the text from the DSTC2 restaurant reservation dataset and create code-mixed versions of it in Hindi-English, Bengali-English, Gujarati-English and Tamil-English. We also establish initial baselines on this dataset using existing state of the art models. This dataset along with our baseline implementations will be made publicly available for research purposes.
%U https://aclanthology.org/C18-1319
%P 3766-3780

Markdown (Informal)

[A Dataset for Building Code-Mixed Goal Oriented Conversation Systems](https://aclanthology.org/C18-1319) (Banerjee et al., COLING 2018)

• A Dataset for Building Code-Mixed Goal Oriented Conversation Systems (Banerjee et al., COLING 2018)

ACL

• Suman Banerjee, Nikita Moghe, Siddhartha Arora, and Mitesh M. Khapra. 2018. A Dataset for Building Code-Mixed Goal Oriented Conversation Systems. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3766–3780, Santa Fe, New Mexico, USA. Association for Computational Linguistics.