Empowering Multilingual AI: Cross-Lingual Transfer Learning

Authors

  • Pawan Sen Assistant Professor, Computer Science Engineering, Arya Institute of Engineering And Technology, Jaipur, Raj. Author
  • Rohit Sharma Assistant Professor, Deaprtment of ECE, Arya Institute of Engineering, Technology and Management, Jaipur, Raj. Author
  • Lucky Verma Science Student, Prince School, Govindpura, Jhotwara, Jaipur, Raj. Author
  • Pari Tenguriya Science Student, Shri Agarsen Public School, Jaipur, Raj Author

DOI:

https://doi.org/10.61841/gkmmgp67

Keywords:

Natural Language Processing, Cross-Lingual, Multilingual, Machine, Communication

Abstract

Multilingual natural language processing (NLP) and cross-lingual transfer learning have emerged as pivotal fields in the realm of language technology. This abstract explores the essential concepts and methodologies behind these areas, shedding light on their significance in a world characterized by linguistic diversity. Multilingual NLP enables machines to process and generate text in multiple languages, breaking down communication barriers and fostering global collaboration. Cross-lingual transfer learning, on the other hand, leverages knowledge from one language to enhance NLP tasks in another, facilitating efficient resource utilization and improved model performance. The abstract highlights the growing relevance of these approaches in a multilingual and interconnected world, underscoring their potential to reshape the future of natural language understanding and communication. 

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References

1. J. Liu and F. G. Fang, "Perceptions, awareness, and perceived effects of home culture on intercultural communication:

Perspectives of university students in China," System, vol. 67, pp. 25-37, 2017.

2. S. M. Yimam, C. Biemann, S. Malmasi, G. Paetzold, L. Specia, S. Štajner, et al., "A report on the complex word

identification shared task 2018," Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building

Educational Applications, 2018.

3. Sherstinsky, "Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network," Physica

D:Nonlinear Phenomena, vol. 404, pp. 132306, Mar 2020.

4. Vaswani, N., Shazeer, N., Parmar, J., Uszkoreit, L., Jones, A., N. Gomez, et al., "Attention is all you need," Advances in

Neural information processing systems, pp. 5998-6008, 2017.

5. K. C. Sheang, "Multilingual complex word identification: Convolutional neural networks with morphological and

linguistic features," Proceedings of the Student Research Workshop Associated with RANLP 2019

6. K. O'Shea and R. Nash, "An introduction to convolutional neural networks," ArXiv e-prints, 2015.

7. J. Pennington, R. Socher and C. Manning, "GloVe: Global vectors for word representation," Proceedings of the 2014

Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532-1543, Oct. 2014.

8. Y. Kim, Y. Gao and H. Ney, "Effective cross-lingual transfer of neural machine translation models without shared

vocabularies," Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1246-

1257, 2019.

9. Z. Liu, G. I. Winata, P. Xu and P. Fung, Coach: A coarse-to-fine approach for cross-domain slot filling, 2020. [online]

Available:

10. S. Gella, D. Elliott and F. Keller, "Cross-lingual visual verb sense disambiguation," Proceedings of the 2019

Conference of the North American Chapter of the Association for Computational Linguistics: Human Language

Technologies Volume 1 (Long and Short Papers), pp. 1998-2004, 2019.

11. R. K. Kaushik Anjali and D. Sharma, "Analyzing the Effect of Partial Shading on Performance of Grid Connected

Solar PV System," 2018 3rd International Conference and Workshops on Recent Advances and Innovations in

Engineering (ICRAIE), pp. 1-4, 2018.

12. R. Kaushik, O. P. Mahela, P. K. Bhatt, B. Khan, S. Padmanaban and F. Blaabjerg, "A Hybrid Algorithm for

Recognition of Power Quality Disturbances," in IEEE Access, vol. 8, pp. 229184-229200, 2020.

13. Kaushik, R. K. "Pragati: Analysis and Case Study of Power Transmission and Distribution." J Adv Res Power Electro

Power Sys 7.2 (2020): 1-3.

14. Sharma, R. and Kumar, G. (2017) “Availability improvement for the successive K-out-of-N machining system using

standby with multiple working vacations,” International journal of reliability and safety,

15. Gireesh, K., Manju, K. and Preeti (2016), “Maintenance policies for improving the availability of a software-hardware

system,” in 2016 11th International Conference on Reliability, Maintainability and Safety (ICRMS). IEEE.

Jain, M., Kaushik, M. and Kumar, G. (2015). “Reliability analysis for embedded system with two types of faults and common cause failure using Markov processes

16. In Proceedings of the Sixth International Conference on Computer and Communication Technology 2015. New York, NY, USA: ACM.

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Published

31.05.2020

How to Cite

Sen, P., Sharma, R., Verma, L., & Tenguriya, P. (2020). Empowering Multilingual AI: Cross-Lingual Transfer Learning. International Journal of Psychosocial Rehabilitation, 24(3), 7915-7918. https://doi.org/10.61841/gkmmgp67