ARCHIVES
Case Study
Advanced Multilingual Chatbot for Indian Language Support
Dr. Avinash. S. Kapse1
Vaishali Datta Parihar2
1 2 Department of Computer Science and Engineering, Anuradha Collage of Engineering & Technology, Chikhli, Maharashtra, India.
Published Online: January-April 2026
Pages: 478-491
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501054
References
1. IAMAI, "Annual Internet in India Report 2023," Internet and Mobile Association of India, 2023.
2. A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, "Bag of tricks for efficient text classification," in Proc. 15th Conf. Eur. Chapter Assoc.
Comput. Linguistics (EACL), 2017, pp. 427–431.
3. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," in
Proc. NAACL-HLT, 2019, pp. 4171–4186.
4. D. Kakwani, A. Kunchukuttan, S. Golla, N. C. Gokul, A. Bhatt, M. M. Khapra, and P. Kumar, "IndicNLPSuite: Monolingual corpora,
evaluation benchmarks and pre-trained multilingual language models for Indian languages," in Findings of EMNLP 2020, pp. 4948–4961.
5. J. D. Williams and S. Young, "Partially observable Markov decision processes for spoken dialog systems," Computer Speech & Language,
vol. 21, no. 2, pp. 393–422, 2007.
6. K. Bhogale, A. Raman, T. Javed, S. Doddapaneni, A. Kunchukuttan, P. Kumar, and M. M. Khapra, "IndicVoices: Towards building the
largest multilingual TTS and ASR dataset for Indic languages," arXiv:2303.01535, 2023.
7. Bhashini, "Bhashini API documentation," Ministry of Electronics and IT, Government of India, 2023. Available: https://bhashini.gov.in
8. A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, "Robust speech recognition via large-scale weak supervision,"
arXiv:2212.04356, 2022Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., "Attention is all you need," in Advances
in Neural Information
9. Processing Systems (NeurIPS), vol. 30, 2017.
10. T. Bocklisch, J. Faulkner, N. Pawlowski, and A. Nichol, "Rasa: Open source language understanding and dialogue management,"
arXiv:1712.05181, 2017.
11. S. Khanuja, D. Bansal, S. Mehtani, S. Khosla, A. Dey, B. Gopalan, et al., "MuRIL: Multilingual representations for Indian languages,"
arXiv:2103.10730, 2021.
12. R. Dabre, A. Kunchukuttan, D. Kakwani, and A. Bhatt, "IndicBART: A pre-trained model for natural language generation of Indic
languages," arXiv:2212.05409, 2022.
13. I. A. Bhat, R. A. Bhat, M. Bhat, and S. Sengupta, "Universal dependency parsing for Hindi-English code switching," in Proc. NAACL-HLT,
2018, pp. 987–998.
14. G. Ramesh, S. Doddapaneni, A. Bheemaraj, M. Jobanputra, R. AK, A. Sharma, et al., "Samanantar: The largest publicly available parallel
corpora collection for 11 Indic languages," arXiv:2104.05596, 2021.
15. A. Kunchukuttan, P. Mehta, and P. Bhattacharyya, "The IIT Bombay English-Hindi parallel corpus," in Proc. LREC, 2018.
16. AI4Bharat, "IndicNLP Library," 2023. Available: https://github.com/AI4Bharat/indic-nlp-library
17. J. Carletta, "Assessing agreement on classification tasks: The kappa statistic," Computational Linguistics, vol. 22, no. 2, pp. 249–254, 1996.
18. Sarvam AI, "Sarvam AI API documentation," 2024. Available: https://www.sarvam.ai
19. Google Cloud, "Cloud Text-to-Speech API documentation," 2024. Available: https://cloud.google.com/text-to-speech
20. A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, and T. Mikolov, "FastText.zip: Compressing text classification models,"
arXiv:1612.03651, 2016
2. A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, "Bag of tricks for efficient text classification," in Proc. 15th Conf. Eur. Chapter Assoc.
Comput. Linguistics (EACL), 2017, pp. 427–431.
3. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," in
Proc. NAACL-HLT, 2019, pp. 4171–4186.
4. D. Kakwani, A. Kunchukuttan, S. Golla, N. C. Gokul, A. Bhatt, M. M. Khapra, and P. Kumar, "IndicNLPSuite: Monolingual corpora,
evaluation benchmarks and pre-trained multilingual language models for Indian languages," in Findings of EMNLP 2020, pp. 4948–4961.
5. J. D. Williams and S. Young, "Partially observable Markov decision processes for spoken dialog systems," Computer Speech & Language,
vol. 21, no. 2, pp. 393–422, 2007.
6. K. Bhogale, A. Raman, T. Javed, S. Doddapaneni, A. Kunchukuttan, P. Kumar, and M. M. Khapra, "IndicVoices: Towards building the
largest multilingual TTS and ASR dataset for Indic languages," arXiv:2303.01535, 2023.
7. Bhashini, "Bhashini API documentation," Ministry of Electronics and IT, Government of India, 2023. Available: https://bhashini.gov.in
8. A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, "Robust speech recognition via large-scale weak supervision,"
arXiv:2212.04356, 2022Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., "Attention is all you need," in Advances
in Neural Information
9. Processing Systems (NeurIPS), vol. 30, 2017.
10. T. Bocklisch, J. Faulkner, N. Pawlowski, and A. Nichol, "Rasa: Open source language understanding and dialogue management,"
arXiv:1712.05181, 2017.
11. S. Khanuja, D. Bansal, S. Mehtani, S. Khosla, A. Dey, B. Gopalan, et al., "MuRIL: Multilingual representations for Indian languages,"
arXiv:2103.10730, 2021.
12. R. Dabre, A. Kunchukuttan, D. Kakwani, and A. Bhatt, "IndicBART: A pre-trained model for natural language generation of Indic
languages," arXiv:2212.05409, 2022.
13. I. A. Bhat, R. A. Bhat, M. Bhat, and S. Sengupta, "Universal dependency parsing for Hindi-English code switching," in Proc. NAACL-HLT,
2018, pp. 987–998.
14. G. Ramesh, S. Doddapaneni, A. Bheemaraj, M. Jobanputra, R. AK, A. Sharma, et al., "Samanantar: The largest publicly available parallel
corpora collection for 11 Indic languages," arXiv:2104.05596, 2021.
15. A. Kunchukuttan, P. Mehta, and P. Bhattacharyya, "The IIT Bombay English-Hindi parallel corpus," in Proc. LREC, 2018.
16. AI4Bharat, "IndicNLP Library," 2023. Available: https://github.com/AI4Bharat/indic-nlp-library
17. J. Carletta, "Assessing agreement on classification tasks: The kappa statistic," Computational Linguistics, vol. 22, no. 2, pp. 249–254, 1996.
18. Sarvam AI, "Sarvam AI API documentation," 2024. Available: https://www.sarvam.ai
19. Google Cloud, "Cloud Text-to-Speech API documentation," 2024. Available: https://cloud.google.com/text-to-speech
20. A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, and T. Mikolov, "FastText.zip: Compressing text classification models,"
arXiv:1612.03651, 2016
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