| 本文已被：浏览 4438次 下载 7505次 |
码上扫一扫！ |
|
|
| 层次短语翻译中基于Markov 随机场的层次切分模型 |
|
刘乐茂1,2, 赵铁军1,2, 曹海龙1,2, 朱聪慧1,2, 张春越1,2
|
|
1.哈尔滨工业大学 计算机科学与技术学院,黑龙江 哈尔滨 150001;2.语言语音教育部-微软重点实验室(哈尔滨工业大学),黑龙江 哈尔滨 150001
|
|
| 摘要: |
| 翻译推导的切分歧义是统计机器翻译面临的一个很重要的问题,而在层次短语机器翻译中,其尤为突出.提出了一个层次切分模型来处理推导的切分歧义性.采用Markov 随机场构建模型,然后将其融入层次短语翻译模型,以便自动选择更合理的切分.在NIST 中英翻译的任务中,该模型的训练效率高,通过NIST05,NIST06 和NIST08这3 个测试集上的翻译效果表明,该模型提高了层次短语翻译的性能. |
| 关键词: 层次短语翻译 切分模型 图模型 Markov 随机场 依存树 |
| DOI：10.3724/SP.J.1001.2012.04207 |
| 分类号: |
| 基金项目:国家自然科学基金(60736014, 61173073, 61100093); 国家高技术研究发展计划(863)(2011AA01A207) |
|
| Hierarchical Partition Model Based on Markov Random Fields for Hierarchical Phrase- Based Machine Translation |
|
LIU Le-Mao1,2, ZHAO Tie-Jun1,2, CAO Hai-Long1,2, ZHU Cong-Hui1,2, ZHANG Chun-Yue1,2
|
|
1.School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;2.MOE-MS Key Laboratory of Natural Language Processing and Speech (Harbin Institute of Technology), Harbin 150001, China
|
| Abstract: |
| The partition ambiguity of translation derivations is an important problem suffered by the statistical machine translation, and it is much more important in a hierarchical phrase-based machine translation. In the paper, a hierarchical partition model is proposed to address the problem. The study applies markov random fields to construct the model, and integrate it into the hierarchical translation model to automatically select the more reasonable partition. In the NIST Chinese-English translation tasks, the optimization of the model is very efficient, and it improves the translation performance for hierarchical phrase-based translation on NIST05, NIST06 and NIST08 test sets. |
| Key words: hierarchical phrase translation partition model graphical model Markov random fields dependency tree |
