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Extreme Learning Machine for Huge Hypotheses Re-ranking in Statistical Machine Translation
Liu, Yan1; Vong, Chi Man1; Wong, Pak Kin2
2017-04
Source PublicationCOGNITIVE COMPUTATION
ISSN1866-9956
Volume9Issue:2Pages:285-294
Abstract

In statistical machine translation (SMT), a possibly infinite number of translation hypotheses can be decoded from a source sentence, among which re-ranking is applied to sort out the best translation result. Undoubtedly, re-ranking is an essential component of SMT for effective and efficient translation. A novel re-ranking method called Scaled Sorted Classification Re-ranking (SSCR) based on extreme learning machine (ELM) classification and minimum error rate training (MERT) is proposed. SSCR contains four steps: (1) the input features are normalized to the range of 0 to 1; (2) an ELM classification model is constructed for hypothesis ranking; (3) each translation hypothesis is ranked using the ELM classification model; and (4) the highest ranked subset of hypotheses are selected, in which the hypothesis with best predicted score based on MERT (system score) is returned as the final translation result. Compared with the baseline score (lower bound), SSCR with ELM classification can raise the translation quality up to 6.7% in IWSLT 2014 Chinese to English corpus. Compared with the state-of-the-art rank boosting, SSCR has a relatively 7.8% of improvement on BLEU in a larger WMT 2015 English-to-French corpus. Moreover, the training time of the proposed method is about 160 times faster than traditional regression-based re-ranking.

KeywordRe-ranking Extreme Learning Machine Scaled Sorted Classification Re-ranking Statistical Machine Translation
DOI10.1007/s12559-017-9452-x
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000398994500010
PublisherSPRINGER
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:5   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Affiliation1.Department of Computer and Information Science, University of Macau, Macau, China
2.Department of Electromechanical Engineering, University of Macau, Macau, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Liu, Yan,Vong, Chi Man,Wong, Pak Kin. Extreme Learning Machine for Huge Hypotheses Re-ranking in Statistical Machine Translation[J]. COGNITIVE COMPUTATION,2017,9(2):285-294.
APA Liu, Yan,Vong, Chi Man,&Wong, Pak Kin.(2017).Extreme Learning Machine for Huge Hypotheses Re-ranking in Statistical Machine Translation.COGNITIVE COMPUTATION,9(2),285-294.
MLA Liu, Yan,et al."Extreme Learning Machine for Huge Hypotheses Re-ranking in Statistical Machine Translation".COGNITIVE COMPUTATION 9.2(2017):285-294.
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