Abstract
As a typical low-resource language pair, besides severely limited by the scale of parallel corpus, Chinese-Lao language pair also has considerable linguistic differences, resulting in poor performance of Chinese-Lao neural machine translation (NMT) task. However, compared with the Chinese-Lao language pair, there are considerable cross-lingual similarities between Thai-Lao languages. According to these features, we propose a novel NMT approach. We first train Chinese-Thai and Thai-Lao NMT models wherein Thai is treated as pivot language. Then the transfer learning strategy is used to extract the encoder and decoder respectively from the two trained models. Finally, the encoder and decoder are combined into a new model and then fine-tuned based on a small-scale Chinese-Lao parallel corpus. We argue that the pivot language Thai can deliver more information to Lao decoder via linguistic similarity and help improve the translation quality of the proposed transfer-based approach. Experimental results demonstrate that our approach achieves 9.12 BLEU on Chinese-Lao translation task using a small parallel corpus, compared to the 7.37 BLEU of state-of-the-art Transformer baseline system using back-translation.
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Acknowledgements
We would like to thank the anonymous reviewers for their constructive comments. The work is supported by National Natural Science Foundation of China (Grant Nos. 61732005, 61672271, 61761026, 61762056 and 61866020), National key research and development plan project (Grant No. 2019QY1800), Yunnan high-tech industry development project (Grant No. 201606), and Natural Science Foundation of Yunnan Province (Grant No. 2018FB104).
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Yu, Z., Yu, Z., Huang, Y., Guo, J., Wang, Z., Man, Z. (2020). Transfer Learning for Chinese-Lao Neural Machine Translation with Linguistic Similarity. In: Li, J., Way, A. (eds) Machine Translation. CCMT 2020. Communications in Computer and Information Science, vol 1328. Springer, Singapore. https://doi.org/10.1007/978-981-33-6162-1_1
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DOI: https://doi.org/10.1007/978-981-33-6162-1_1
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