模糊匹配在树到串翻译模型中的应用  

Fuzzy Matching for Tree-Based Machine Translation

作　　者：熊皓[1] 刘洋[1] 刘群[1] 

机构地区：[1]中国科学院计算技术研究所智能信息处理重点实验室,北京100190

出　　处：《中文信息学报》2011年第2期66-71,共6页Journal of Chinese Information Processing

基　　金：国家自然科学基金重点资助项目(60736014);国家863计划重点资助项目(2006AA010108)

摘　　要：在传统的基于树的翻译模型中,一般都是将一条规则视为字符串,然后使用字符串匹配技术从规则表中搜索可用的规则。然而,由于基于树的翻译模型依赖于句法分析的结果,而有些语言的句法分析准确率并不是很高,所以由句法分析错误造成的规则无法匹配的现象很常见,特别是在树到树的翻译模型中,能够精确匹配的规则数量非常稀少,进而对机器翻译的性能造成很大影响。因此该文提出了一种基于树核的模糊匹配技术,在NIST 2005汉英翻译测试集上的结果表明,基于树核的模糊匹配模型相对于传统的翻译模型显著的提高了1.3个BLEU值,并且在森林模型中使用模糊匹配技术仍然能够提高0.7个BLEU值。Previous related work of tree-based models treat rules as strings and then match rules using string matching algorithm.However,the performance of tree-based models is largely dependent on the parsing results,and for some languages,the precision of current parser is still far from state-of-the-art.So two rules with one different tag causing by parsing errors seems to be unmatchable.Under exact matching strategy,the size of available rules is implicitly scarce especially in tree-to-tree models,in which the performance is still unacceptable.In this paper,we present a tree kernel based fuzzy matching algorithm which computes the similarity between different rules.Experimental results on NIST 2005 Chinese-to-English test set show that our system achieve an absolute improvement of 1.3% in term of BLEU score over string matching system.Furthermore,when using the packed forest,our method still gets a relative improvement of 0.7 BLEU score.

关 键 词：树核 树到串翻译模型 统计机器翻译 模糊匹配 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]