CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
A New Input Method for Human Translators: Integrating Machine Translation Effectively and Imperceptibly
Guoping, Huang; Jiajun, Zhang; Yu, Zhou; Chengqing, Zong; Chengqing Zong
2015-07
Conference NameThe Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)
Source PublicationProceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)
Conference Date2015-7-25
Conference PlaceBuenous Aires, Argentina
AbstractComputer-aided translation (CAT) system is the most popular tool which helps human translators perform language translation efficiently. To further improve the efficiency, there is an increasing interest in applying the machine translation (MT) technology to upgrade CAT. Post-editing is a standard approach: human translators generate the translation by correcting MT outputs. In this paper, we propose a novel approach deeply integrating MT into CAT systems: a well-designed input method which makes full use of the knowledge adopted by MT systems, such as translation rules, decoding hypotheses and n-best translation lists. Our proposed approach allows human translators to focus on choosing better translation results with less time rather than just complete translation themselves. The extensive experiments demonstrate that our method saves more than 14% time and over 33% keystrokes, and it improves the translation quality as well by more than 3 absolute BLEU scores compared with the strong baseline, i.e., post-editing using Google Pinyin.
KeywordMachine Translation Input Method
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14810
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorChengqing Zong
AffiliationNational Laboratory of Pattern Recognition, CASIA, Beijing, P.R. China
Recommended Citation
GB/T 7714
Guoping, Huang,Jiajun, Zhang,Yu, Zhou,et al. A New Input Method for Human Translators: Integrating Machine Translation Effectively and Imperceptibly[C],2015.
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