'Exploring the Effectiveness of Transfer Learning for Text Classification in Low-Resource Languages'

Registration ID: IJNRD_195153

Published ID: IJNRD2305404

DOI: Click Here to Get

Sayyed Aamir Hussain , Dr. Nilotpal Chakraborty

transfer learning, text classification, low-resource languages, pre-trained language models, Nepali language.

Text classification is an essential task in natural language processing (NLP) that involves assigning a category or label to a given text. While significant progress has been made in text classification for high-resource languages, low-resource languages still pose a significant challenge. In this paper, we explore the effectiveness of transfer learning techniques for text classification in low-resource languages. Specifically, we investigate the performance of pre-trained language models, such as BERT and GPT, on a text classification task in a low-resource language. We conduct experiments on a publicly available dataset of news articles in the Nepali language, which is a low-resource language. We compare the performance of several transfer learning models against traditional machine learning models and baseline models. Our results show that transfer learning models outperform the traditional machine learning models and the baseline models, achieving an F1 score of 0.87. We also perform an ablation study to investigate the effect of different training strategies and model architectures on the performance of transfer learning models. Our findings suggest that fine-tuning a pre-trained language model on a small amount of task-specific data is an effective strategy for text classification in low-resource languages. Overall, our study highlights the potential of transfer learning for text classification in low-resource languages and provides valuable insights for researchers and practitioners working on NLP tasks in low-resource settings. Keywords: Transfer learning, text classification, low-resource languages, pre-trained language models, Nepali language.

Published Paper   E-Certificate

Sayyed Aamir Hussain & Dr. Nilotpal Chakraborty (May-2023). 'Exploring the Effectiveness of Transfer Learning for Text Classification in Low-Resource Languages'. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), e24-e49. https://ijnrd.org/papers/IJNRD2305404.pdf

Volume 8 Issue 5, May-2023

Pages : e24-e49

Paper Reg. ID: IJNRD_195153

Published Paper Id: IJNRD2305404

Downloads: 000121976

Research Area: Computer Science & Technology 

Country: Indore, Madhya Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305404.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305404

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.

Indexing Coverage includes Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many more recognized academic repositories.

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.

Subject Category: Research Area

Call for Paper: More Details