Abstract
Neural machine translation (NMT) has produced very promising results on various high resource languages that have sizeable parallel datasets. However, low resource languages that lack sufficient parallel datasets face challenges in the automated translation filed. The main part of NMT is a recurrent neural network, which can work with sequential data at word and sentence levels, given that sequences are not too long. Due to the large number of word and sequence combinations, a parallel dataset is required, which unfortunately is not always available, particularly for low resource languages. Therefore, we adapted a character neural translation model that was based on a combined structure of recurrent neural network and convolutional neural network. This model was trained on the IWSLT 2016 Arabic—English and the IWSLT 2015 English—Vietnamese datasets. The model produced encouraging results particularly on the Arabic datasets, where Arabic is considered a rich morphological language.
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References
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Lamb, A., Xie, M.: Convolutional encoders for neural machine translation. WEB download (2016)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Gulcehre, C., Firat, O., Xu, K., Cho, K., Barrault, L., Lin, H.C., Bougares, F., Schwenk, H., Bengio, Y.: On using monolingual corpora in neural machine translation. arXiv preprint arXiv:1503.03535 (2015)
Ramachandran, P., Liu, P.J., Le, Q.V.: Unsupervised pretraining for sequence to sequence learning. arXiv preprint arXiv:1611.02683 (2016)
Bradbury, J., Merity, S., Xiong, C., Socher, R.: Quasi-recurrent neural networks. arXiv preprint arXiv:1611.01576 (2016)
Wang, X., Liu, Y., Sun, C., Wang, B., Wang, X.: Predicting polarities of tweets by composing word embeddings with long short-term memory. In: ACL, no. 1, pp. 1343–1353 (2015)
Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649–657 (2015)
Chen, Y., Liu, Y., Cheng, Y., Li, V.O.: A teacher-student framework for zero-resource neural machine translation. arXiv preprint arXiv:1705.00753 (2017)
He, D., Xia, Y., Qin, T., Wang, L., Yu, N., Liu, T., Ma, W.Y.: Dual learning for machine translation. In: Advances in Neural Information Processing Systems, pp. 820–828 (2016)
Wu, J., Hou, H., Shen, Z., Du, J., Li, J.: Adapting attention-based neural network to low-resource Mongolian-Chinese machine translation. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC -2016. LNCS (LNAI), vol. 10102, pp. 470–480. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_39
Belinkov, Y., Glass, J.: Large-scale machine translation between arabic and hebrewcorpora and initial results. arXiv preprint arXiv:1609.07701 (2016)
Almahairi, A., Cho, K., Habash, N., Courville, A.: First result on arabic neural machine translation. arXiv preprint arXiv:1606.02680 (2016)
Firat, O., Cho, K., Bengio, Y.: Multi-way, multilingual neural machine translation with a shared attention mechanism. arXiv preprint arXiv:1601.01073 (2016)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. arXiv preprint arXiv:1705.03122 (2017)
Cettolo, M., Girardi, C., Federico, M.: WIT3: web inventory of transcribed and translated talks. In: Proceedings of the 16th Conference of the European Association for Machine Translation (EAMT), pp. 261–268 (2012)
Hong, V.T., Thuong, H.V., Le Tien, T., Pham, L.N., Van, V.N.: The English-Vietnamese Machine Translation System for IWSLT 2015 (2015)
Tiedemann, J.: Parallel data, tools and interfaces in OPUS. In: LREC, vol. 2012, pp. 2214–2218 (2012)
Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
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, Association for Computational Linguistics, pp. 311–318 (2002)
Neubig: Tips on building neural machine translation systems (2016). https://www.kantanmt.com/whatisbleuscore.php
Klein, G., Kim, Y., Deng, Y., Senellart, J., Rush, A.M.: Opennmt: open-source toolkit for neural machine translation. arXiv preprint arXiv:1701.02810 (2017)
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Almansor, E.H., Al-Ani, A. (2018). A Hybrid Neural Machine Translation Technique for Translating Low Resource Languages. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_26
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