Tackling Low-Resource Languages: Efficient Transfer Learning Techniques for Multilingual NLP

Vinodh Gunnam

doi:10.36676/jrps.v13.i4.1601

Authors

Vinodh Gunnam Independent Researcher

DOI:

https://doi.org/10.36676/jrps.v13.i4.1601

Keywords:

Tackling Low-Resource Languages, Transfer Learning Techniques

Abstract

Therefore, it aims to review the most efficient techniques for transfer learning about low-resource language in multilingual NLP. Some languages need reliable data; the problem is that they lack the resources to achieve high model accuracy. One of the solutions presented is transfer learning, a technique that enables knowledge from other high-resource languages to be utilized for LRLs. This study also uses simulation reports, real-time case studies, and experiences to support how these techniques work. The major issues are also outlined, such as lack of training data, model complexity and language problems of variation and solutions, data augmentation, few shots learning, and pre-trained multilingual models. These approaches make way for more diverse NLP systems, and they help pave the way for language inclusion.

References

Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., ... & Gelly, S. (2019, May). Parameter-efficient transfer learning for NLP. In International conference on machine learning (pp. 2790-2799). PMLR. http://proceedings.mlr.press/v97/houlsby19a/houlsby19a.pdf

Vasa, Y., Mallreddy, S. R., & Jami, V. S. (2022). AUTOMATED MACHINE LEARNING FRAMEWORK USING LARGE LANGUAGE MODELS FOR FINANCIAL SECURITY IN CLOUD OBSERVABILITY. International Journal of Research and Analytical Reviews , 9(3), 183–190.

Vasa, Y., & Singirikonda, P. (2022). Proactive Cyber Threat Hunting With AI: Predictive And Preventive Strategies. International Journal of Computer Science and Mechatronics, 8(3), 30–36.

Katikireddi, P. M., Singirikonda, P., & Vasa, Y. (2021). Revolutionizing DEVOPS with Quantum Computing: Accelerating CI/CD pipelines through Advanced Computational Techniques. Innovative Research Thoughts, 7(2), 97–103. https://doi.org/10.36676/irt.v7.i2.1482

Vasa, Y., Cheemakurthi, S. K. M., & Kilaru, N. B. (2022). Deep Learning Models For Fraud Detection In Modernized Banking Systems Cloud Computing Paradigm. International Journal of Advances in Engineering and Management, 4(6), 2774–2783. https://doi.org/10.35629/5252-040627742783

Vasa, Y., & Mallreddy, S. R. (2022). Biotechnological Approaches To Software Health: Applying Bioinformatics And Machine Learning To Predict And Mitigate System Failures. Natural Volatiles & Essential Oils, 9(1), 13645–13652. https://doi.org/https://doi.org/10.53555/nveo.v9i2.5764

Mallreddy, S. R., & Vasa, Y. (2022). Autonomous Systems In Software Engineering: Reducing Human Error In Continuous Deployment Through Robotics And AI. NVEO - Natural Volatiles & Essential Oils, 9(1), 13653–13660. https://doi.org/https://doi.org/10.53555/nveo.v11i01.5765

Vasa, Y., Jaini, S., & Singirikonda, P. (2021). Design Scalable Data Pipelines For Ai Applications. NVEO - Natural Volatiles & Essential Oils, 8(1), 215–221. https://doi.org/https://doi.org/10.53555/nveo.v8i1.5772

Singirikonda, P., Jaini, S., & Vasa, Y. (2021). Develop Solutions To Detect And Mitigate Data Quality Issues In ML Models. NVEO - Natural Volatiles & Essential Oils, 8(4), 16968–16973. https://doi.org/https://doi.org/10.53555/nveo.v8i4.5771

Vasa, Y. (2021). Develop Explainable AI (XAI) Solutions For Data Engineers. NVEO - Natural Volatiles & Essential Oils, 8(3), 425–432. https://doi.org/https://doi.org/10.53555/nveo.v8i3.5769

Vasa, Y. (2021b). Quantum Information Technologies in cybersecurity: Developing unbreakable encryption for continuous integration environments. International Journal for Research Publication and Seminar, 12(2), 482–490. https://doi.org/10.36676/jrps.v12.i2.1539

Vasa, Y. (2021b). Robustness and adversarial attacks on generative models. International Journal for Research Publication and Seminar, 12(3), 462–471. https://doi.org/10.36676/jrps.v12.i3.1537

Vasa, Y., Mallreddy, S. R., & Jami, V. S. (2022). AUTOMATED MACHINE LEARNING FRAMEWORK USING LARGE LANGUAGE MODELS FOR FINANCIAL SECURITY IN CLOUD OBSERVABILITY. International Journal of Research and Analytical Reviews , 9(3), 183–190.

Nunnagupala, L. S. C. ., Mallreddy, S. R., & Padamati, J. R. . (2022). Achieving PCI Compliance with CRM Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 13(1), 529–535.

Jangampeta, S., Mallreddy, S.R., & Padamati, J.R. (2021). Anomaly Detection for Data Security in SIEM: Identifying Malicious Activity in Security Logs and User Sessions. 10(12), 295-298

Jangampeta, S., Mallreddy, S. R., & Padamati, J. R. (2021). Data Security: Safeguarding the Digital Lifeline in an Era of Growing Threats. International Journal for Innovative Engineering and Management Research, 10(4), 630-632.

Sukender Reddy Mallreddy(2020).Cloud Data Security: Identifying Challenges and Implementing Solutions.JournalforEducators,TeachersandTrainers,Vol.11(1).96 -102.

Kilaru, N. B., Cheemakurthi, S. K. M., & Gunnam, V. (n.d.). Advanced Anomaly Detection In Banking: Detecting Emerging Threats Using Siem. International Journal of Computer Science and Mechatronics, 7(4), 28–33.

Kilaru, N. B., Cheemakurthi, S. K. M., & Gunnam, V. (2021). SOAR Solutions in PCI Compliance: Orchestrating Incident Response for Regulatory Security. ESP Journal of Engineering & Technology Advancements, 1(2), 78–84. https://doi.org/10.56472/25832646/ESP-V1I2P111

Gunnam, V., & Kilaru, N. B. (2021). Securing Pci Data: Cloud Security Best Practices And Innovations. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal| NVEO.

Kilaru, N. B., & Cheemakurthi, S. K. M. (2021). Techniques For Feature Engineering To Improve Ml Model Accuracy. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal| NVEO, 194-200.

Naresh Babu Kilaru. (2021). AUTOMATE DATA SCIENCE WORKFLOWS USING DATA ENGINEERING TECHNIQUES. International Journal for Research Publication and Seminar, 12(3), 521–530. https://doi.org/10.36676/jrps.v12.i3.1543