Abstract
One of difficulties in Chinese-English machine translation is that the grammatical meaning expressed by morphology or syntax in target translations is usually determined by Chinese function words or word order. In order to address this issue, we develop classifiers to automatically detect usages of common Chinese function words based on Chinese Function usage Knowledge Base (CFKB) and initially propose a function word usage embedding model to incorporate detection results into neural machine translation (NMT). Experiments on the NIST Chinese-English translation task demonstrate that the proposed method can obtain significant improvements on the quality of both translation and word alignment over the NMT baseline.
This work is partially supported by National Basic Research Program of China (2014CB340504), National Natural Science Foundation of China (No. 61402419, No. 60970083), National Social Science Foundation (No. 14BYY096), Basic research project of Science and Technology Department of Henan Province (No. 142300410231, No. 142300410308) and science and technology project of Science and Technology Department of Henan Province (No. 172102210478).
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Notes
- 1.
Extracted from LDC2002E18, LDC2003E07, LDC2003E14, Hansards portion of LDC2004T07, LDC2004T08 and LDC2005T06.
- 2.
We observe that the “concat” and “usage” method perform better than the “part” method in terms of both translation and alignment quality in “DE”’s experiment. Therefore we don’t have the experiments of “part” method with the other function words.
- 3.
After the Chinese sentence is its reference translation, “BASE” means baseline model, “FNMT” means model with function word usages, text after “/” is the usage of Chinese function words.
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We thank the anonymous reviewers for their insightful comments and suggestions.
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Zhang, K., Xu, H., Xiong, D., Liu, Q., Zan, H. (2018). Improving Chinese-English Neural Machine Translation with Detected Usages of Function Words. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_64
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