Abstract
Due to the lack of structured language resources, low-resource language machine translation often faces difficulties in cross-language semantic paraphrasing. In order to solve the problem of low-resource machine translation from Indonesian to Chinese, a cognate-parallel-corpus-based expanding method of language resources is proposed, and an improved neural machine translation model is trained by the Malay-corpus-enhanced corpus. The improved model can achieve a comparable result as that of Google in the experiment of Indonesian-Chinese machine translation. The experimental results also show that the morphological similarity and semantic equivalence between the languages are very effective computational features to improve the performance of neural machine translation for low-resource languages.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Liu, Y.: Recent advances in neural machine translation. J. Comput. Res. Dev. 54(6), 1144–1149 (2017)
Liu, W., Wang, L., Zhang, X.: Fast-syntax-matching-based Japanese-Chinese limited machine translation. In: Gelbukh, A. (ed.) CICLing 2016. LNCS, vol. 9624, pp. 63–73. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75487-1_6
King, B.P.: Practical Natural Language Processing for Low-Resource Languages. Doctoral dissertation, University of Michigan (2015)
Liu, W.: supervised ensemble learning for vietnamese tokenization. Int. J. Uncertainty, Fuzziness Knowl.-Based Syst. 25(2), 285–299 (2017)
Cieri, C., Liberman, M.: TIDES language resources: a resource map for translingual information access. In: Proceedings of the 3rd International Conference on Language Resources and Evaluation (LREC), pp. 1334–1339. ELRA (2002)
Olive, J., Christianson, C., McCary, J.: Handbook of Natural Language Processing and Machine Translation: DARPA Global Autonomous Language Exploitation. Springer, New york (2011). ISBN 9781441977120
Nakov, P., Ng, H.T.: Improving statistical machine translation for a resource-poor language using related resource-rich languages. J. Artif. Intell. Res. 44, 179–222 (2012)
Koehn, P.: Statistical Machine Translation. Cambridge University Press, Cambridge (2009). ISBN 9780521874151
Bies, A., et al.: A comparison of event representations in DEFT. In: Coreference, and Representation Proceedings of the 4th Workshop on Events: Definition, Detection, pp. 27–36. ACL (2016)
Cieri, C., Maxwell, M., Strassel, S., Tracey, J.: Selection criteria for low resource language programs. In: Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC), pp. 4543–4549. ELRA (2016)
Knill, K.M., Gales, M.J.F., Ragni, A., Rath, S.P.: Language independent and unsupervised acoustic models for speech recognition and keyword spotting. In: Proceedings the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH), pp. 16–20. ISCA (2014)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Hirschberg, J., Manning, C.D.: Advances in natural language processing. Science 349(6245), 261–266 (2015)
Vaswani, A., et al.: Attention Is All You Need. arXiv:1706.03762v5 (2017)
Artetxe, M., Labaka, G., Agirre, E., Cho, K.: Unsupervised Neural Machine Translation. arXiv:1710.11041v1 (2017)
Wu, Y.: Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv:1609.08144v2 (2016)
Manning, C.D.: Last words: computational linguistics and deep learning. Comput. Linguist. 41(4), 701–707 (2015)
Zhang, J., Zong, C.: Deep neural networks in machine translation: an overview. IEEE Intell. Syst. 30(5), 16–25 (2015)
Liu, W., Lin, L.: Probabilistic ensemble learning for vietnamese word segmentation. In: Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 931–934. ACM (2014)
Koehn, P., Hoang, H., Birch, A., et al.: Moses: open source toolkit for statistical machine translation. In: Annual Meeting of the Association for Computational Linguistics (ACL), Demonstration Session, Prague, Czech Republic, June 2007
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of the 28th Annual Conference on Neural Information Processing Systems (NIPS), pp. 3104–3112. Curran Associates (2014)
Neubig, G.: Neural Machine Translation and Sequence-to-sequence Models: A Tutorial. arXiv:1703.01619v1 (2017)
Acknowledgements
The research is supported by the Key Project of State Language Commission of China (No. ZDI135-26), the Natural Science Foundation of Guangdong Province (No. 2018A030313672), the Featured Innovation Project of Guangdong Province (No. 2015KTSCX035), the Bidding Project of Guangdong Provincial Key Laboratory of Philosophy and Social Sciences (No. LEC2017WTKT002), and the Key Project of Guangzhou Key Research Base of Humanities and Social Sciences: Guangzhou Center for Innovative Communication in International Cities (No. 2017-IC-02).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, W., Wang, L. (2019). Malay-Corpus-Enhanced Indonesian-Chinese Neural Machine Translation. In: Peng, H., Deng, C., Wu, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2018. Communications in Computer and Information Science, vol 986. Springer, Singapore. https://doi.org/10.1007/978-981-13-6473-0_21
Download citation
DOI: https://doi.org/10.1007/978-981-13-6473-0_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6472-3
Online ISBN: 978-981-13-6473-0
eBook Packages: Computer ScienceComputer Science (R0)