融合词法句法分析联合模型的树到串EBMT方法

• Published in: Beijing da xue xue bao Vol. 53; no. 2; pp. 295 - 304
• Main Authors: Wang, Dandan, Xu, Jin'an, Chen, Yufeng, Zhang, Yujie, Yang, Xiaohui
• Format: Journal Article
• Language: Chinese
• Published: Beijing Acta Scientiarum Naturalium Universitatis Pekinenis 01.01.2017; 北京交通大学计算机与信息技术学院,北京,100044
• Subjects: Language; Segmentation; 联合模型; 依存树到串模型; example-based machine translation; 基于实例的机器翻译; 泛化模板; generalization template; dependency tree-to-string model; joint model
• ISSN: 0479-8023
• DOI: 10.13209/j.0479-8023.2017.035

In consideration of the complexity and high cost of system construction in traditional examplebased machine translation (EBMT) methods, the authors propose a Chinese-English tree-to-string EBMT method. Compared with the traditional methods, the preposed approach just needed to implement the processing of source language parsing. Word segmentation, POS tagging and dependency parsing were jointed to relieve the affections of error propagation and failure of feature extraction at different levels. Moreover, the authors extracted and generalized bilingual word and phase alignments from examples and templates by using the dependency structure of source language. Experimental results show that the preposed method can achieve better performance significantly than baseline systems.