会议专题

Learning from User Feedback for Machine Translation in Real-Time

  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 offline baseline system with all training data.

Guoping Huang Yu Zhou Jiajun Zhang Chengqing Zong

National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beiji National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beiji

国际会议

第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)

昆明

英文

1-12

2016-12-02(万方平台首次上网日期,不代表论文的发表时间)