MACHINE TRANSLATION: SANSKRIT TO ENGLISH
DOI:
https://doi.org/10.61841/jc747e73Keywords:
Machine translation,, Paninan Framework,, Neural machine translationAbstract
Translation is the key for communication among countries on a global scale. Machine translation is the process of translating the source language to the target language performed by a computer. Machine translation can be performed by applying a variety of techniques or approaches each of which has its own set of benefits and disadvantages.This paper proposes to perform machine translation from Sanskrit to English. The development of the machine translation system for Sanskrit, being an ancient language is a challenging task. Sanskrit is one of the oldest languages in the world and is now not widely in use, yet a large number of ancient texts are written in the language and hence a translation system will prove to be crucial in their translation and understanding. This paper proposes to incorporate the Paninian f r a m e w o r k i n t o a n e u r a l machine t r a n s l a t i o n system
Downloads
References
1. Paul Kiparsky. 2009. on the architecture of Panini’s grammar. In Gérard Huet, Amba Kulkarni, and Peter Scharf, editors, Sanskrit Computational Linguistics, Springer, Berlin, Heidelberg, pages 33–94
2. Kak, Subhash C.. “The Paninian approach to natural language processing.” Int. J. Approx. Reasoning 1 (1987): 117-130.
3. Bharati, A., Chaitanya, V., & Sangal, R. (1994). Paninian framework and its application toAnusaraka. Sadhana, 19(1), 113–127.doi:10.1007/bf02760393
4. Mujadia, V., Agarwal, D., Mamidi, R., & Sharma, D. M. (2015). Paninian grammar based hindi dialogue anaphora resolution. 2015 International Conference on Asian Language Processing(IALP). doi:10.1109/ialp.2015.7451530
5. Akshar Bharati Rajeev Sangal “Parsing Free Word Order Languages in the Paninian Framework,” ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics Pages 105 -111, June 22 - 26, 1993
6. Amita and Ajay Jangra. “An A n n o t a t i o n S c h e m e for EnglishLanguage using Paninian Framework.” (2015).
7. S. K. Gorthi, A. Palakurthi, R. Mamidi, and D. M. Sharma.Identification of karaka relations in an english sentence. Proceedings of ICON, 2014
8. Tapaswi, N., & Jain, S. (2012, September). Treebank based deep grammar acquisition and Part-Of-Speech Tagging for Sanskrit sentences. In Software Engineering (CONSEG), 2012 CSI Sixth nternational Conference on (pp. 1 -4). IEEE
9. “English to Sanskrit Machine Translator: Lexical Parser “ Prof P.R. Devale ,Ms.Vaishali M. Barkade, international journal on computer science and engineering(IJCSE) ISSN-0975-5462,Vol.02,NO 10
10. “English to Sanskrit Machine Translator Semantic Mapper “ Prof P.R.Devale ,Ms.Vaishali M. Barkade. International Journal of EngineeringScience and Technology. 2.
11. Interlingua based Sanskrit-English Machine Translation “Ms.Sreedeepa ,Dr. Sumam Mary Idicula 2017 International Conference on circuits Power and Computing Technologies[ICCPCT]
12. Bahadur, Promila & Jain, Ajai & Singh Chauhan, Durg. (2015). Architecture of English to Sanskrit machine translation.
13. Bahadur, P., Jain, A., & Chauhan, D. S. (2011). English to Sanskrit machine translation. Proceedings of the International Conference & Workshop on Emerging Trends in Technology - ICWET’11.doi:10.1145/1980022.1980161
14. Mishra, V., & Mishra, R. B. (2012). English to Sanskrit machine translation system: a rule-based approach. International Journal of Advanced Intelligence Paradigms, 4(2),168.doi:10.1504/ijaip.2012.048144
15. Rathod S.G., Sondur S., “English to Sanskrit Translator and Synthesizer (ETSTS)”, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 12, December 2012
16. Warhade, Sandeep R., Bharati Vidyapeeth and Prakash Devale. “English-to-Sanskrit Statistical Machine Translation with Ubiquitous Application.” (2012).
17. Mishra, Vimal and Mishra, R. B., ‘Study of Example based English to Sanskrit Machine Translation’, Journal of Research and Development in Computer Science and Engineering, Polibits
18. Journal Article Ganesh R. Pathak Sachin P. Godse “English to Sanskrit Machine Translation Using Transfer Based approach” 2010 AIP Conference Proceedings
19. Mishra, Vimal and Ravi Mishra. “ANN and Rule Based Model for English to Sanskrit Machine Translation.” (2010).
20. Cho, Kyunghyun, Bart van Merrienboer, Dzmitry Bahdanau and Yoshua Bengio. “On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.” SSST@EMNLP (2014).
21. Wu, Yonghui, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Gregory S. Corrado, Macduff Hughes and Jeffrey Dean. “Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.” CoRR abs/1609.08144 (2016): n. pag.
22. Sundermeyer, Martin, Ralf Schlüter and Hermann Ney. “LSTMNeural Networks for Language Modeling.” INTERSPEECH (2012).
23. Merity, Stephen, Nitish Shirish Keskar and Richard Socher. “Regularizing and Optimizing LSTM Language Models.” CoRR abs/1708.02182 (2017): n. pag.
24. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS’14, pages 3104–3112, Cambridge, MA, USA, 2014.
25. K. Papineni et al. 2002. BLEU: a method for automatic evaluation of machine translation. In Proc. of the 40th ACL, pages 311–318.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
