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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2019,3,31]]},"abstract":"<jats:p>Computer-aided translation (CAT) systems are the most popular tool for helping human translators efficiently perform language translation. To further improve the translation efficiency, there is an increasing interest in applying machine translation (MT) technology to upgrade CAT. To thoroughly integrate MT into CAT systems, in this article, we propose a novel approach: a new input method that makes full use of the knowledge adopted by MT systems, such as translation rules, decoding hypotheses, and n-best translation lists. The proposed input method contains two parts: a phrase generation model, allowing human translators to type target sentences quickly, and an n-gram prediction model, helping users choose perfect MT fragments smoothly. In addition, to tune the underlying MT system to generate the input method preferable results, we design a new evaluation metric for the MT system. The proposed input method integrates MT effectively and imperceptibly, and it is particularly suitable for many target languages with complex characters, such as Chinese and Japanese. The extensive experiments demonstrate that our method saves more than 23% in time and over 42% in keystrokes, and it also improves the translation quality by more than 5 absolute BLEU scores compared with the strong baseline, i.e., post-editing using Google Pinyin.<\/jats:p>","DOI":"10.1145\/3230638","type":"journal-article","created":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T08:20:16Z","timestamp":1542097216000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Input Method for Human Translators"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6481-6250","authenticated-orcid":false,"given":"Guoping","family":"Huang","sequence":"first","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Tencent AI Lab, Shenzhen"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajun","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Tencent AI Lab, Shenzhen"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Tencent AI Lab, Shenzhen"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengqing","family":"Zong","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Nanshan District, Shenzhen"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,11,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1162\/coli.2008.07-055-R2-06-29"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 8th International NLPSC Workshop. 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A web-based interactive computer aided translation tool. In Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL-IJCNLP\u201909). 17--20. Philipp Koehn. 2009b. A web-based interactive computer aided translation tool. 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