English Language Technologies and the Preservation of Literary Diversity in Multilingual India

Ravindra Hanmantrao Sagar
Assistant Professor
Nagnath Arts, Commerce and Science College, Aundha

Nagnath, Distt: Hingoli, Maharashtra

Abstract  :  India’s literary heritage is distributed across a highly diverse linguistic landscape involving multiple language families, scripts, oral traditions, regional literary cultures, and unequal levels of digital representation. English has historically functioned as an important bridge language in higher education, publishing, translation, administration, and digital communication, and contemporary language technologies frequently employ English either as a training-rich language, interface language, or translation pivot. This study examines how English-mediated language technologies contribute to the preservation, accessibility, and circulation of literary diversity in multilingual India while also assessing the risks of technological imbalance for low-resource languages. A quantitative research design was used with 320 respondents comprising university teachers, researchers, librarians, translators, publishers, and digital humanities practitioners. Five technological dimensions were examined: machine translation, optical character recognition and digitisation, digital archives and search, speech technologies, and dependence on English-mediated technological infrastructure. The proposed Literary Diversity Preservation Index demonstrated satisfactory internal reliability. Results indicated a strong positive relationship between overall language-technology adoption and perceived literary preservation (r = .68, p < .001). Machine translation, digitisation/OCR, and digital archival accessibility emerged as the strongest positive predictors, whereas excessive dependence on English-centric technological pathways showed a modest negative association with equitable representation of low-resource literary traditions. The findings suggest that English language technologies can serve as a powerful bridge for preservation and wider circulation, but sustainable literary diversity requires direct investment in Indian-language corpora, scripts, metadata, speech resources, culturally sensitive translation, and community-controlled digital archives.

Keywords: Language Technology, Literary Diversity, Multilingual India, Machine Translation, Digital Preservation, Indian Languages, English-Mediated Technology

References

1. Aggarwal, D., Gupta, V., & Kunchukuttan, A. (2022). IndicXNLI: Evaluating multilingual inference for Indian languages. Proceedings of EMNLP 2022, 10994–11006. DOI: 10.18653/v1/2022.emnlp-main.755. ACL Anthology source
2. Dabre, R., & Kunchukuttan, A. (2024). Findings of WMT 2024’s MultiIndic22MT shared task for machine translation of 22 Indian languages. Proceedings of the Ninth Conference on Machine Translation, 669–676. DOI: 10.18653/v1/2024.wmt-1.55. ACL Anthology source
3. Dabre, R., Shrotriya, H., Kunchukuttan, A., Puduppully, R., Khapra, M., & Kumar, P. (2022). IndicBART: A pre-trained model for Indic natural language generation. Findings of ACL 2022, 1849–1863. DOI: 10.18653/v1/2022.findings-acl.145. ACL Anthology source
4. Doddapaneni, S., Aralikatte, R., Ramesh, G., Goyal, S., Khapra, M. M., Kunchukuttan, A., & Kumar, P. (2023). Towards leaving no Indic language behind: Building monolingual corpora, benchmark and models for Indic languages. Proceedings of ACL 2023. DOI: 10.18653/v1/2023.acl-long.693. ACL Anthology source
5. Gala, J., Chitale, P. A., Raghavan, A. K., Gumma, V., Doddapaneni, S., Kumar, A., et al. (2023). IndicTrans2: Towards high-quality and accessible machine translation models for all 22 scheduled Indian languages. Transactions on Machine Learning Research. TMLR/OpenReview paper
6. Kakwani, D., Kunchukuttan, A., Golla, S., N.C., G., Bhattacharyya, A., Khapra, M. M., & Kumar, P. (2020). IndicNLPSuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for Indian languages. Findings of EMNLP 2020, 4948–4961. DOI: 10.18653/v1/2020.findings-emnlp.445. ACL Anthology source
7. Kanojia, D., Dabre, R., Dewangan, S., Bhattacharyya, P., Haffari, G., & Kulkarni, M. (2020). Harnessing cross-lingual features to improve cognate detection for low-resource languages. Proceedings of COLING 2020, 1384–1395. DOI: 10.18653/v1/2020.coling-main.119. ACL Anthology source
8. Khemchandani, Y., Mehtani, S., Patil, V., Awasthi, A., Talukdar, P., & Sarawagi, S. (2021). Exploiting language relatedness for low web-resource language model adaptation: An Indic languages study. Proceedings of ACL-IJCNLP 2021. DOI: 10.18653/v1/2021.acl-long.105. ACL Anthology source
9. Madhani, Y., Khapra, M. M., & Kunchukuttan, A. (2023). Bhasa-Abhijnaanam: Native-script and romanized language identification for 22 Indic languages. Proceedings of ACL 2023. DOI: 10.18653/v1/2023.acl-short.71. ACL Anthology source
10. Office of the Registrar General & Census Commissioner, India. (2011). C-16: Population by Mother Tongue, India. Ministry of Home Affairs, Government of India. Official Census of India dataset
11. Ramesh, G., Doddapaneni, S., Bheemaraj, A., Jobanputra, M., Raghavan, A. K., Sharma, A., et al. (2022). Samanantar: The largest publicly available parallel corpora collection for 11 Indic languages. Transactions of the Association for Computational Linguistics, 10. ACL Anthology record
12. Siripragada, S., Philip, J., Namboodiri, V. P., & Jawahar, C. V. (2020). A multilingual parallel corpora collection effort for Indian languages. Proceedings of LREC 2020. ACL Anthology source
13. Srivastava, N., Mukhopadhyay, R., K R, P., & Jawahar, C. V. (2020). IndicSpeech: Text-to-speech corpus for Indian languages. Proceedings of the Twelfth Language Resources and Evaluation Conference, 6417–6422. ACL Anthology source
14. Digital India Bhashini Division. (2022–2026). BHASHINI: National Language Translation Mission. Digital India Corporation, Ministry of Electronics and Information Technology, Government of India. Official BHASHINI source
15. (2024). Digital preservation of Indigenous languages: At the intersection of technology and culture. United Nations Educational, Scientific and Cultural Organization. UNESCO source
16. (2024). Hello Indigenous: A blueprint on the preservation of endangered Indigenous languages through digital inclusion. UNESCO. UNESCO publication