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Learning from User Feedback for Machine Translation in Real-Time
Guoping, Huang1,2; Jiajun, Zhang1; Yu, Zhou1; Chengqing, Zong1; Chengqing Zong
2016-07
会议名称The Fifth Conference on Natural Language Processing and Chinese Computing & The Twenty Fourth International Conference on Computer Processing of Oriental Languages(NLPCC-ICCPOL 2016)
会议录名称Natural Language Understanding and Intelligent Applications: 5th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2016, and 24th International Conference on Computer Processing of Oriental Languages, ICCPOL 2016, Kunming, China, December 2--6, 2016, Proceedings
会议日期2016-7-30
会议地点Kunming, China
摘要Post-editing is the most popular approach to improve accuracy and speed of human translators by applying the machine translation (MT) technology. During the translation process, human translators generate the translation by correcting MT outputs in the post-editing scenario. To avoid repeating the same MT errors, in this paper, we propose an efficient framework to update MT in real-time by learning from user feedback. This framework includes: (1) an anchor-based word alignment model, being specially designed to get correct alignments for unknown words and new translations of known words, for extracting the latest translation knowledge from user feedback; (2) an online translation model, being based on random forests (RFs), updating translation knowledge in real-time for later predictions and having a strong adaptability with temporal noise as well as context changes. The extensive experiments demonstrate that our proposed framework significantly improves translation quality as the number of feedback sentences increasing, and the translation quality is comparable to that of the off-line baseline system with all training data.
关键词Machine Translation Online Learning Random Forests
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14812
专题模式识别国家重点实验室_自然语言处理
通讯作者Chengqing Zong
作者单位1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Guoping, Huang,Jiajun, Zhang,Yu, Zhou,et al. Learning from User Feedback for Machine Translation in Real-Time[C],2016.
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