ARCHIVES

1. UNESCO, Education in Mother Tongue Improves Learning Outcomes, UNESCO Report, 2021.
2. Vaswani, A., et al. (2017). Attention is All You Need. Advances in Neural Information Processing Systems (NeurIPS).
3. Devlin, J., et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT.
4. Kakwani, D., et al. (2020). IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language
Models for Indian Languages. Findings of EMNLP.
5. Fan, A., et al. (2021). Beyond English-Centric Multilingual Machine Translation. Journal of Machine Learning Research (M2M-100).
6. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All You
Need. Advances in Neural Information Processing Systems (NeurIPS).
7. Pires, T., Schlinger, E., & Garrette, D. (2019). How Multilingual is Multilingual BERT? Proceedings of ACL.
8. Wu, S., & Dredze, M. (2019). Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT. EMNLP.
9. Conneau, A., et al. (2020). Unsupervised Cross-lingual Representation Learning at Scale (XLM-R). ACL.
10. Kakwani, D., Kunchukuttan, A., Golla, S., et al. (2020). IndicBERT: A Multilingual ALBERT Model for Indian Languages.
arXiv:2007.03750.
11. Wolf, T., et al. (2020). Transformers: State-of-the-Art Natural Language Processing. EMNLP.
12. K. Papineni et al., “BLEU: A Method for Automatic Evaluation of Machine Translation,” ACL, 2002
13. S. Banerjee and A. Lavie, “METEOR: An Automatic Metric for MT Evaluation,” ACL Workshop, 2005.
14. M. Snover et al., “Translation Edit Rate,” AMTA, 2006.