Title
A New Input Method for Human Translators: Integrating Machine Translation Effectively and Imperceptibly.
Abstract
Computer-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.
Year
Venue
Field
2015
IJCAI
Rule-based machine translation,Example-based machine translation,Language translation,Computer science,Evaluation of machine translation,Machine translation,Algorithm,Machine translation software usability,Transfer-based machine translation,Natural language processing,Artificial intelligence,Computer-assisted translation
DocType
Citations 
PageRank 
Conference
3
0.39
References 
Authors
7
4
Name
Order
Citations
PageRank
Guoping Huang132.08
Jiajun Zhang225746.34
Yu Zhou3144.00
Chengqing Zong41004102.38