﻿<?xml version="1.0" encoding="utf-8"?>
<ArticleSet>
  <ARTICLE>
    <Journal>
      <PublisherName>مرکز منطقه ای اطلاع رسانی علوم و فناوری</PublisherName>
      <JournalTitle>Journal of Information Systems and Telecommunication (JIST) </JournalTitle>
      <ISSN>2322-1437</ISSN>
      <Volume>14</Volume>
      <Issue>1</Issue>
      <PubDate PubStatus="epublish">
        <Year>2026</Year>
        <Month>5</Month>
        <Day>5</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Gated Fusion Transformer for English-Hindi Multimodal Translation</ArticleTitle>
    <VernacularTitle>Gated Fusion Transformer for English-Hindi Multimodal Translation</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>8</LastPage>
    <ELocationID EIdType="doi">10.66224/jist.50948.14.1.1</ELocationID>
    <Language>en</Language>
    <AuthorList>
      <Author>
        <FirstName>Prianka</FirstName>
        <LastName>Suram</LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName>Pramoda</FirstName>
        <LastName>Patro</LastName>
        <Affiliation>SR University</Affiliation>
      </Author>
    </AuthorList>
    <History PubStatus="received">
      <Year>2025</Year>
      <Month>7</Month>
      <Day>26</Day>
    </History>
    <Abstract>&lt;p&gt;Machine translation is fundamental in closing the gap between different languages, especially in the areas of con- cern and expertise such as agriculture. With the increase of digital tool usages in the agricultural practice, such an accurate and context-sensitive translation is increasingly significant. Proper delivery of agricultural information, including farm methods, weather advisories, and crop suggestions is essential among farmers, farm laborers, policymakers and researchers. Nevertheless, typical text-based translation frameworks tend to be less than optimal because of uncertainness and a restricted knowledge of context. To address these shortcomings, the proposed study refers to Multimodal Machine Translation (MMT) to incorporate textual and visual information to enhance accuracy. Gated Fusion Transformer (GFT) model has been customized to the agricultural field so that the problem of ambiguity in contexts and inconsistencies in translation can be eliminated. Training and evaluation were done using the multilingual benchmark dataset known as FLORES-200. Two commonly employed measures of performance were used, i.e. BLEU and METEOR. The system under proposal produced a BLEU of 58.2; METEOR score of 0.71, a high level and contextually relevant translation indicator. Besides benchmarking the GFT model in agricultural terms, this work adds value to the research community by offering a basis on which future development of multimodal translation systems in low-resource settings with domain-specific applications may be done.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="Keyword">
        <Param Name="Value">Machine Translation</Param>
      </Object>
      <Object Type="Keyword">
        <Param Name="Value">Domain Specific Translation</Param>
      </Object>
      <Object Type="Keyword">
        <Param Name="Value">Multimodal Machine Translation</Param>
      </Object>
      <Object Type="Keyword">
        <Param Name="Value">Multi Modal Fusion Mechanisms</Param>
      </Object>
      <Object Type="Keyword">
        <Param Name="Value">Gated Fusion Transformer</Param>
      </Object>
      <Object Type="Keyword">
        <Param Name="Value">Agricultural Translation</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="Pdf">http://jist.ir/fa/Article/Download/50948</ArchiveCopySource>
  </ARTICLE>
</ArticleSet>