Abstract
Neural machine translation has drastically improved the accuracy of machine translation in recent years. The issue of translating out-of-vocabulary proper nouns (OOV-NNP) is still a hindrance to the betterment of machine translation. In this paper, we introduce neural machine translation followed by Proper Noun Transliteration (NMT-NNPT) to address this issue. We explore the idea of transliteration as a post-processing task on the result of neural machine translation using English–Hindi language pair. This approach further improves the translation accuracy and can be used with any language pair.
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. ICLR 1–15 (2015)
Saini, S., Sahula, V.: Neural machine translation for English to Hindi. In: 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), Kota Kinabalu, pp. 1–6 (2018)
Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. CoRR (2016). abs/1609.08144
Cho, K., Merrienboer, B., Gulcehre, C., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. CoRR (2014). arXIV:abs/1406.1078
Sutskever, L., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advan. Neural Inf. Process. Syst. 27, 3104–3112 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Cho, K., Van Merrieenboer, B., Gulcehre, C., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. CoRR (2014). arXIV:abs/1406.1078
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR (2014). arXIV:abs/1412.3555
Knight, K., Graehl, J.: Machine transliteration. Comput. Linguist. 24(4), 599–612 (1998)
Ekbal, A., Naskar, S.K., Bandyopadhyay, S.: Named entity transliteration. Int. J. Comput. Process. Lang. 20(04), 289–310 (2007)
Tennage, P., Herath, A., Thilakarathne, M., Sandaruwan, P., Ranathunga, S.: Transliteration and byte pair encoding to improve Tamil to Sinhala neural machine translation. In: Moratuwa Engineering Research Conference (MERCon), pp. 390–395 (2018)
Loper, E., Bird, S.: Natural language toolkit
Kunchukuttan, A., Mehta, P., Bhattacharyya, P.: The IIT Bombay English-Hindi parallel corpus. CoRR (2017). arXIV:abs/1710.02855
Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318 (2002)
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Narang, H., Gharpure, P. (2020). A GRU-Based Neural Machine Translation Followed by Proper Noun Transliteration. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_31
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DOI: https://doi.org/10.1007/978-981-15-2475-2_31
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