融合主题的汉越冶金领域统计机器翻译方法

doi:10.3969/j.issn.1000-3428.2017.12.033

计算机工程 ›› 2017, Vol. 43 ›› Issue (12): 179-183.doi: 10.3969/j.issn.1000-3428.2017.12.033

融合主题的汉越冶金领域统计机器翻译方法

周珂,余正涛,高盛祥

(昆明理工大学信息工程与自动化学院,昆明 650500)

收稿日期:2016-11-14 出版日期:2017-12-15 发布日期:2017-12-15
作者简介:周珂(1991—),男,硕士研究生,主研方向为机器翻译;余正涛,教授、博士生导师;高盛祥(通信作者),博士。
基金项目:
国家自然科学基金(61672271,61761026,61732005);昆明理工大学校人才培养项目(省级)(KKSY201703005)。

Statistical Machine Translation Method Integrating Topic for Chinese-Vietnamese Metallurgy Field

ZHOU Ke,YU Zhengtao,GAO Shengxiang

(School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)

Received:2016-11-14 Online:2017-12-15 Published:2017-12-15

摘要/Abstract

摘要： 为有效利用领域知识提高汉越冶金领域的机器翻译效果,利用术语与主题的分布关系约束术语的选择,提出融合主题的统计机器翻译方法。建立术语翻译模型,抽取短语概率表时将术语对源语言文档的主题信息融入到翻译概率表中,利用主题分布描述术语与主题之间的关系,同时使用冶金领域语料训练翻译模型,将2个模型融合到基于短语的汉越翻译系统解码过程中,指导选择出最符合领域特性的术语及译文。融合模型前后的对比实验结果表明,与基于短语的机器翻译方法相比,该方法可有效提高汉越冶金领域的翻译性能。

关键词: 冶金领域, 统计机器翻译, 主题模型, 术语, 联合翻译模型

Abstract: In order to effectively use domain knowledge to improve the effect of Chinese-Vietnamese metallurgy field translation,this paper proposes a method of statistical machine translation,which uses the terms and topics’ distribution relation to constrain the choice of terms.It constructs term translation model in the process of phrase probability table extraction,the topic information of the source language document for the term pair is incorporated into the translation probability table,and the relationship between the term and the topic is described by topic distribution.Based on the translation model of metallurgy field corpus training,the two models are integrated into the decoding process of Chinese-Vietnamese phrase-based translation system to guide the selection of terms and translations that best match the domain characteristics.The contrastive experiments are carried out before and after the fusion model.The results show that,compared with the phrase-based machine translation method,the proposed method can increase translation performance in the Chinese-Vietnamese metallurgy field effectively.

Key words: metallurgy field, statistical machine translation, topic model, term, united translation model

中图分类号:

TP18

周珂,余正涛,高盛祥. 融合主题的汉越冶金领域统计机器翻译方法 [J]. 计算机工程, 2017, 43(12): 179-183.

ZHOU Ke,YU Zhengtao,GAO Shengxiang. Statistical Machine Translation Method Integrating Topic for Chinese-Vietnamese Metallurgy Field [J]. Computer Engineering, 2017, 43(12): 179-183.

参考文献

参考文献［1］张军.我国西南地区在“一带一路”开放战略中的优势及定位［J］.经济纵横,2014(11):93-96. ［2］刘群.统计机器翻译综述［J］.中文信息学报,2003,17(4):1-12. ［3］KOEHN P,OCH F J,MARCU D.Statistical Phrase-based Translation［C］//Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology.Michigan,USA:Association for Computational Linguistics,2003:127-133. ［4］YAMADA K.A Syntax-based Translation Model［C］//Proceedings of Meeting of the Association for Computational Linguistics.Michigan,USA:Association for Computational Linguistics,2002:1-5. ［5］张冬梅,刘小蝶,晋耀红.基于模板的汉英专利机器翻译研究［J］.计算机应用研究,2013,30(7):2044-2046. ［6］晋耀红.一种混合策略的专利机器翻译系统研究［J］.计算机工程与应用,2012,48(4):29-32. ［7］MENG F,XIONG D,JIANG W,et al.Modeling Term Translation for Document-informed Machine Translation［C］//Proceedings of Conference on Empirical Methods in Natural Language Processing.Doha,Qatar:［s.n.］,2014:546-556. ［8］KOEHN P,OCH F J,MARCU D.Statistical Phrase-based Translation［C］//Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Tech-nology Michigan,USA:Association for Computational Linguistics,2002:48-54. ［9］CHIANG D.A Hierarchical Phrase-based Model for Statistical Machine Translation［C］//Proceedings of Meeting of the Association for Computational Linguistics.Michigan,USA:Association for Computational Linguistics,2005:263-270. ［10］BLEI D M,NG A Y,JORDAN M I.Latent Dirichlet Allocation［J］.Journal of Machine Learning Research,2003,3:993-1022. ［11］DARROCH J N,RATCLIFF D.Generalized Iterative Scaling for Log-linear Models［J］.Annals of Math-ematical Statistics,1972,43(5):1470-1480. ［12］KOEHN P,SCHROEDER J.Experiments in Domain Adaptation for Statistical Machine Translation［C］//Proceedings of the Workshop on Statistical Machine Translation.Michigan,USA:Association for Computational Linguistics,2007:224-227. ［13］BROWN P F,PIETRA S A D P,PIETRA V J D.The Mathematics of Statistical Machine Translation:Parameter Estimation［J］.Computational Linguistics,1993,19(2):263-311. ［14］OCH F J.Minimum Error Rate Training in Statistical Machine Translation［C］//Proceedings of the 41st Annual Meeting on Association for Computational Linguistics.Michigan,USA:Association for Computational Linguistics,2003:160-167. ［15］KOEHN P,HOANG H,BIRCH A,et al.Moses:Open Source Toolkit for Statistical Machine Translation［C］//Proceedings of Meeting of the ACL on Interactive Poster and Demonstration Sessions.Michigan,USA:Associa-tion for Computational Linguistics,2007:177-180. ［16］PAPINENI K,ROUKOS S,WARD T,et al.BLEU:A Method for Automatic Evaluation of Machine Transla-tion［C］//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics.Michigan,USA:Association for Computational Linguistics,2002:311-318. 编辑金胡考

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[11]	严锐,李石君. 基于查询意图识别与主题建模的文档检索算法[J]. 计算机工程, 2018, 44(3): 189-194.
[12]	吴旭康,杨旭光,陈园园,王营冠,张阅川. 主题联合词向量模型[J]. 计算机工程, 2018, 44(2): 233-237,270.
[13]	许腾腾,黄恒君. 一种改进的Supervised-LDA文本模型及其应用 [J]. 计算机工程, 2018, 44(1): 69-73,78.
[14]	张志远,杨宏敬,赵越. 基于吉布斯采样结果的主题文本网络构建方法[J]. 计算机工程, 2017, 43(6): 150-157.
[15]	孙冠东,张兵,刘禹岍,熊贇. 基于载客数据的出租车热门区域功能发现[J]. 计算机工程, 2017, 43(5): 16-22.

融合主题的汉越冶金领域统计机器翻译方法

Statistical Machine Translation Method Integrating Topic for Chinese-Vietnamese Metallurgy Field

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