<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="patil-etal-2024-srib">
    <titleInfo>
        <title>SRIB-NMT’s Submission to the Indic MT Shared Task in WMT 2024</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Pranamya</namePart>
        <namePart type="family">Patil</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Raghavendra</namePart>
        <namePart type="family">Hr</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Aditya</namePart>
        <namePart type="family">Raghuwanshi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kushal</namePart>
        <namePart type="family">Verma</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Ninth Conference on Machine Translation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Barry</namePart>
            <namePart type="family">Haddow</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tom</namePart>
            <namePart type="family">Kocmi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Philipp</namePart>
            <namePart type="family">Koehn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christof</namePart>
            <namePart type="family">Monz</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Miami, Florida, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In the context of the Indic Low Resource Ma-chine Translation (MT) challenge at WMT-24, we participated in four language pairs:English-Assamese (en-as), English-Mizo (en-mz), English-Khasi (en-kh), and English-Manipuri (en-mn). To address these tasks,we employed a transformer-based sequence-to-sequence architecture (Vaswani et al., 2017).In the PRIMARY system, which did not uti-lize external data, we first pretrained languagemodels (low resource languages) using avail-able monolingual data before finetuning themon small parallel datasets for translation. Forthe CONTRASTIVE submission approach, weutilized pretrained translation models like In-dic Trans2 (Gala et al., 2023) and appliedLoRA Fine-tuning (Hu et al., 2021) to adaptthem to smaller, low-resource languages, aim-ing to leverage cross-lingual language transfercapabilities (CONNEAU and Lample, 2019).These approaches resulted in significant im-provements in SacreBLEU scores(Post, 2018)for low-resource languages.</abstract>
    <identifier type="citekey">patil-etal-2024-srib</identifier>
    <location>
        <url>https://aclanthology.org/2024.wmt-1.64</url>
    </location>
    <part>
        <date>2024-11</date>
        <extent unit="page">
            <start>747</start>
            <end>750</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T SRIB-NMT’s Submission to the Indic MT Shared Task in WMT 2024
%A Patil, Pranamya
%A Hr, Raghavendra
%A Raghuwanshi, Aditya
%A Verma, Kushal
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F patil-etal-2024-srib
%X In the context of the Indic Low Resource Ma-chine Translation (MT) challenge at WMT-24, we participated in four language pairs:English-Assamese (en-as), English-Mizo (en-mz), English-Khasi (en-kh), and English-Manipuri (en-mn). To address these tasks,we employed a transformer-based sequence-to-sequence architecture (Vaswani et al., 2017).In the PRIMARY system, which did not uti-lize external data, we first pretrained languagemodels (low resource languages) using avail-able monolingual data before finetuning themon small parallel datasets for translation. Forthe CONTRASTIVE submission approach, weutilized pretrained translation models like In-dic Trans2 (Gala et al., 2023) and appliedLoRA Fine-tuning (Hu et al., 2021) to adaptthem to smaller, low-resource languages, aim-ing to leverage cross-lingual language transfercapabilities (CONNEAU and Lample, 2019).These approaches resulted in significant im-provements in SacreBLEU scores(Post, 2018)for low-resource languages.
%U https://aclanthology.org/2024.wmt-1.64
%P 747-750

Markdown (Informal)

[SRIB-NMT’s Submission to the Indic MT Shared Task in WMT 2024](https://aclanthology.org/2024.wmt-1.64) (Patil et al., WMT 2024)

• SRIB-NMT’s Submission to the Indic MT Shared Task in WMT 2024 (Patil et al., WMT 2024)

ACL

• Pranamya Patil, Raghavendra Hr, Aditya Raghuwanshi, and Kushal Verma. 2024. SRIB-NMT’s Submission to the Indic MT Shared Task in WMT 2024. In Proceedings of the Ninth Conference on Machine Translation, pages 747–750, Miami, Florida, USA. Association for Computational Linguistics.