CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
MinKSR: A Novel MT Evaluation Metric for Coordinating Human Translators with the CAT-Oriented Input Method
Guoping, Huang1,2; Chunlu, Zhao3; Hongyuang, Ma3; Yu, Zhou1; Jiajun, Zhang1
2016-08
Conference NameChina Workshop on Machine Translation (CWMT 2016)
Source PublicationMachine Translation: 12th China Workshop, CWMT 2016, Urumqi, China, August 25--26, 2016, Revised Selected Papers
Conference Date2016-8-25
Conference PlaceUrumqi, China
AbstractIn 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.
KeywordMachine Translation Evaluation Metric Input Method
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14813
Collection模式识别国家重点实验室_自然语言处理
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
3.CNCERT/CC, Beijing, China
Recommended Citation
GB/T 7714
Guoping, Huang,Chunlu, Zhao,Hongyuang, Ma,et al. MinKSR: A Novel MT Evaluation Metric for Coordinating Human Translators with the CAT-Oriented Input Method[C],2016.
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