CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
A Simple, Straightforward and Effective Model for Joint Bilingual Terms Detection and Word Alignment in SMT
Guoping, Huang1,2; Yu, Zhou1; Jiajun, Zhang1; Chengqing, Zong1
2016-07
Conference NameThe Fifth Conference on Natural Language Processing and Chinese Computing & The Twenty Fourth International Conference on Computer Processing of Oriental Languages(NLPCC-ICCPOL 2016)
Source PublicationNatural 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
Conference Date2016-7-30
Conference PlaceKunming, China
AbstractTerms extensively exist in specific domains, and term translation plays a critical role in domain-specific statistical machine translation (SMT) tasks. However, it’s a challenging task to extract term translation knowledge from parallel sentences because of the error propagation in the SMT training pipeline. In this paper, we propose a simple, straightforward and effective model to mitigate the error propagation and improve the quality of term translation. The proposed model goes from initial weak monolingual detection of terms based on naturally annotated resources (e.g. Wikipedia) to a stronger bilingual joint detection of terms, and allows the word alignment to interact. The extensive experiments show that our method substantially boosts the performance of bilingual term detection by more than 8 points absolute F-score. And the term translation quality is substantially improved by more than 3.66% accuracy, as well as the sentence translation quality is significantly improved by 0.38 absolute BLEU points, compared with the strong baseline, i.e. the well tuned Moses.
KeywordMachine Translation Term Detection Word Alignment
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14811
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorChengqing, Zong
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Guoping, Huang,Yu, Zhou,Jiajun, Zhang,et al. A Simple, Straightforward and Effective Model for Joint Bilingual Terms Detection and Word Alignment in SMT[C],2016.
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