Abstract
In order to improve the efficiency of human translation, there is an increasing interest in applying machine translation (MT) to computer assisted translation (CAT). The newly proposed CAT-oriented input method is such a typical approach, which can help translators significantly save keystrokes by exploiting MT deep information, such as n-best candidates, hypotheses and translation rules. In order to further save more keystrokes, we propose in this paper a novel MT evaluation metric for coordinating human translators with the input method. This evaluation metric takes MT deep information into account, and makes longer perfect fragments correspond to fewer keystrokes. Extensive experiments show that the novel evaluation metric makes MT substantially reduce the keystrokes of translating process by accurately grasping deep information for the CAT-oriented input method, and it significantly improves the productivity of human translation compared with BLEU and TER.
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Acknowledgments
The research work has been partially funded by the Natural Science Foundation of China (NSFC) under Grant No. 61403379 and No. 61402123.
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Huang, G., Zhao, C., Ma, H., Zhou, Y., Zhang, J. (2016). MinKSR: A Novel MT Evaluation Metric for Coordinating Human Translators with the CAT-Oriented Input Method. In: Yang, M., Liu, S. (eds) Machine Translation. CWMT 2016. Communications in Computer and Information Science, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-3635-4_1
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DOI: https://doi.org/10.1007/978-981-10-3635-4_1
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