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.
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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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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
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