CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Exploring Diverse Features for Statistical Machine Translation Model Pruning
Tu, Mei; Zhou, Yu; Zong, Chengqing
Source PublicationIEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
2015-11-01
Volume23Issue:11Pages:1847-1857
SubtypeArticle
AbstractIn phrase-based and hierarchical phrase-based statistical machine translation systems, translation performance depends heavily on the size and quality of the translation table. To meet the requirements of making a real-time response, some research has been performed to filter the translation table. However, most existing methods are always based on one or two constraints that act as hard rules, such as not allowing phrase-pairs with low translation probabilities. These approaches sometimes make constraints rigid because they consider only a single factor instead of composite factors. Based on the considerations above, in this paper, we propose a machine learning-based framework that integrates multiple features for translation model pruning. Experimental results show that our framework is effective by pruning 80% of the phrase-pairs and 70% of the hierarchical rules, while retaining the quality of the translation models when using the BLEU evaluation metric. Our study further shows that our method can select the most useful phrase-pairs and rules, including those that are low in frequency but still very useful.
KeywordClassification Statistical Machine Translation (Smt) Syntactic Constraints Translation Model Pruning
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TASLP.2015.2456413
Indexed BySCI
Language英语
WOS Research AreaAcoustics ; Engineering
WOS SubjectAcoustics ; Engineering, Electrical & Electronic
WOS IDWOS:000360835000012
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8979
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorTu, Mei
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Tu, Mei,Zhou, Yu,Zong, Chengqing. Exploring Diverse Features for Statistical Machine Translation Model Pruning[J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,2015,23(11):1847-1857.
APA Tu, Mei,Zhou, Yu,&Zong, Chengqing.(2015).Exploring Diverse Features for Statistical Machine Translation Model Pruning.IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,23(11),1847-1857.
MLA Tu, Mei,et al."Exploring Diverse Features for Statistical Machine Translation Model Pruning".IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 23.11(2015):1847-1857.
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