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Articles

Neural machine translation for Tamil to English

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References

  • Antony, P.J., 2013, March. Machine translation approaches and survey for Indian languages. In International Journal of Computational Linguistics & Chinese Language Processing, Volume 18, Number 1, March 2013.
  • Calvo, H., Rocha-Ramirez, A.P., Moreno-Armendáriz, M.A. and Duchanoy, C.A., 2019. Toward Universal Word Sense Disambiguation Using Deep Neural Networks. IEEE Access, 7, pp.60264-60275.
  • Huang, F., Yates, A., Ahuja, A. and Downey, D., 2011, June. Language models as representations for weakly-supervised nlp tasks. In Proceedings of the fifteenth conference on computational natural language learning (pp. 125-134). Association for Computational Linguistics.
  • Conneau, A. and Lample, G., 2019. Cross-lingual Language Model Pretraining. In Advances in Neural Information Processing Systems (pp. 7057-7067).
  • Pennington, J., Socher, R. and Manning, C.D., 2014, October. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
  • Weaver, W., 1955. Warren Weaver’’s memorandum in 1949: Translation. Milestones in Machine Translation []. Z]. In Locke, WN, Booth, AD (eds.) Machine Translation of Languages: Fourteen Essays.
  • Ghosh, S. and Thamke, S., 2014. Translation of Telugu-Marathi and vice-versa using rule based machine Translation. arXiv preprint arX-iv:1406.3969.
  • Wong, F., Dong, M. and Hu, D., 2006. Machine translation based on translation corresponding tree structure. Tsinghua Science and Technology, 11(1), pp.25-31. doi: 10.1016/S1007-0214(06)70150-X
  • Pandian, L.S. and Kadhirvelu, K., 2012. Machine translation from english to tamil using hybrid technique. Int. J. Comput. Appl, 46, pp.36-42.
  • Shilon, R., 2011. Transfer-based Machine Translation between morphologically-rich and resource-poor languages: The case of Hebrew and Arabic (Doctoral dissertation, Ph. D. thesis, Citeseer).
  • Somers, H., 2003. An overview of EBMT. In Recent advances in example-based machine translation (pp. 3-57). Springer, Dordrecht.
  • Cho, K., Van Merriënboer, B., Bahdanau, D. and Bengio, Y., 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259.
  • Khan, N.J., Anwar, W. and Durrani, N., 2017. Machine translation approaches and survey for indian languages. arXiv preprint arX-iv:1701.04290.
  • Kalchbrenner, N. and Blunsom, P., 2013, October. Recurrent continuous translation models. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1700-1709).
  • Sutskever, I., Vinyals, O. and Le, Q.V., 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems (pp. 3104-3112).
  • Kumar, M.A., Dhanalakshmi, V., Soman, K.P. and Sharmiladevi, V., 2014. Improving the performance of English-Tamil statistical machine translation system using source-side pre-processing. arXiv preprint arXiv:1409.8581.
  • Hans, K. and Milton, R.S., 2016. Improving the performance of neural machine translation involving morphologically rich languages. arX-iv preprint arXiv:1612.02482.
  • Thenmozhi, D., Kumar, B.S. and Aravindan, C., 2018. Deep Learning Approach to English-Tamil and Hindi-Tamil Verb Phrase Translations. In FIRE (Working Notes) (pp. 323-331).
  • Choudhary, H., Pathak, A.K., Saha, R.R. and Kumaraguru, P., 2018, October. Neural machine translation for English-Tamil. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers (pp. 770-775).
  • Tiedemann, J., 2012, May. Parallel Data, Tools and Interfaces in OPUS. In Lrec (Vol. 2012, pp. 2214-2218).
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. In Advances in neural information processing systems (pp. 5998–6008).
  • Pelevina, M., Arefyev, N., Biemann, C. and Panchenko, A., 2017. Making sense of word embeddings. arXiv preprint arXiv:1708.03390.
  • Hochreiter, S., 1998. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02), pp.107-116.
  • Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R. and Schmidhuber, J., 2016. LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), pp.2222-2232. doi: 10.1109/TNNLS.2016.2582924
  • Luong, M.T., Pham, H. and Manning, C.D., 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arX-iv:1508.04025.
  • Klein, G., Kim, Y., Deng, Y., Senellart, J. and Rush, A.M., 2017. Opennmt: Open-source toolkit for neural machine translation. arXiv preprint arXiv:1701.02810.
  • Papineni, K., Roukos, S., Ward, T. and Zhu, W.J., 2002, July. BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 311-318). Association for Computational Linguistics.
  • Rikters, M., Fishel, M. and Bojar, O., 2017. Visualizing neural machine translation attention and confidence. The Prague Bulletin of Mathematical Linguistics, 109(1), pp.39-50. doi: 10.1515/pralin-2017-0037

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