Abstract
Machine translation is used to implement the translation between different languages. Neural machine translation is one of the most popular machine translation methods which have made a great progress in recent years especially on universal languages. However, domestic translation software for non-universal languages is limited and also needs improving. In this paper, we carry out a practice on neural machine translation from Indonesian to Chinese. We first build a bilingual corpus of Indonesian and Chinese. After that, we train translation models using neural machine translation method, with traditional statistical machine translation models as baselines. In the later stages of our practice, we develop a Web software, named as Lore Translator, basing on our so-far best translation model. The performance of our model is comparable to previous work.
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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).
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Bai, L., Liu, W. (2020). A Practice on Neural Machine Translation from Indonesian to Chinese. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_5
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DOI: https://doi.org/10.1007/978-981-13-9406-5_5
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