<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="maurya-desarkar-2023-towards">
    <titleInfo>
        <title>Towards Low-resource Language Generation with Limited Supervision</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kaushal</namePart>
        <namePart type="family">Maurya</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Maunendra</namePart>
        <namePart type="family">Desarkar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Big Picture Workshop</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yanai</namePart>
            <namePart type="family">Elazar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Allyson</namePart>
            <namePart type="family">Ettinger</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nora</namePart>
            <namePart type="family">Kassner</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sebastian</namePart>
            <namePart type="family">Ruder</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Noah</namePart>
            <namePart type="family">A. Smith</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We present a research narrative aimed at enabling language technology for multiple natural language generation (NLG) tasks in low-resource languages (LRLs). With approximately 7,000 languages spoken globally, many lack the resources required for model training. NLG applications for LRLs present two additional key challenges: (i) The training is more pronounced, and (ii) Zero-shot modeling is a viable research direction for scalability; however, generating zero-shot well-formed text in target LRLs is challenging. Addressing these concerns, this narrative introduces three promising research explorations that serve as a step toward enabling language technology for many LRLs. These approaches make effective use of transfer learning and limited supervision techniques for modeling. Evaluations were conducted mostly in the zero-shot setting, enabling scalability. This research narrative is an ongoing doctoral thesis.</abstract>
    <identifier type="citekey">maurya-desarkar-2023-towards</identifier>
    <identifier type="doi">10.18653/v1/2023.bigpicture-1.7</identifier>
    <location>
        <url>https://aclanthology.org/2023.bigpicture-1.7</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>80</start>
            <end>92</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Towards Low-resource Language Generation with Limited Supervision
%A Maurya, Kaushal
%A Desarkar, Maunendra
%Y Elazar, Yanai
%Y Ettinger, Allyson
%Y Kassner, Nora
%Y Ruder, Sebastian
%Y A. Smith, Noah
%S Proceedings of the Big Picture Workshop
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F maurya-desarkar-2023-towards
%X We present a research narrative aimed at enabling language technology for multiple natural language generation (NLG) tasks in low-resource languages (LRLs). With approximately 7,000 languages spoken globally, many lack the resources required for model training. NLG applications for LRLs present two additional key challenges: (i) The training is more pronounced, and (ii) Zero-shot modeling is a viable research direction for scalability; however, generating zero-shot well-formed text in target LRLs is challenging. Addressing these concerns, this narrative introduces three promising research explorations that serve as a step toward enabling language technology for many LRLs. These approaches make effective use of transfer learning and limited supervision techniques for modeling. Evaluations were conducted mostly in the zero-shot setting, enabling scalability. This research narrative is an ongoing doctoral thesis.
%R 10.18653/v1/2023.bigpicture-1.7
%U https://aclanthology.org/2023.bigpicture-1.7
%U https://doi.org/10.18653/v1/2023.bigpicture-1.7
%P 80-92

Markdown (Informal)

[Towards Low-resource Language Generation with Limited Supervision](https://aclanthology.org/2023.bigpicture-1.7) (Maurya & Desarkar, BigPicture 2023)

• Towards Low-resource Language Generation with Limited Supervision (Maurya & Desarkar, BigPicture 2023)

ACL

• Kaushal Maurya and Maunendra Desarkar. 2023. Towards Low-resource Language Generation with Limited Supervision. In Proceedings of the Big Picture Workshop, pages 80–92, Singapore. Association for Computational Linguistics.