... PB/DHPB forests for ; 4 n-gram post-erior features and 1 length posterior feature com-puted from the mixture search space of HM de-coder for ; 1 LM feature; 1 word count feature; ... end for 12: end for 13: for each hypothesis do 14: compute HM decoding features for 15: add to 16: end for 17: for ... end for 11: end for 12: for each hypothesis do 13: compute HM decoding features for 14: add to 15: end for 16: for...
... Model Adaptation for Statisti-cal Machine Translation. Machine Translation, pages77-94.Hua Wu, Haifeng Wang and Chengqing Zong. 2008. Do-main Adaptation forStatisticalMachine Translation with ... Language Modeling Toolkit. In Proc. of ICSLP 2002, pages 901-904.Yik-Cheung Tam, Ian R. Lane and Tanja Schultz. 2007.Bilingual LSA-based adaptation forstatistical machine translation. Machine Translation, ... sourcetoolkit forstatisticalmachine translation. In Proc. ofACL 2007, Demonstration Session, pages 177-180.Yang Liu, Qun Liu and Shouxun Lin. 2006. Tree-to-String Alignment Template forStatistical Machine Translation. ...
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... source toolkit for parsing-based machine translation. In Proceedings of the FourthWorkshop on StatisticalMachine Translation, pages135–139, Athens, Greece, March. Association for Computational ... our “before”system already got the translation correct withoutthe need for the additional phrase translation. Thisis because though the “before” system had neverseen the Urdu expression for ... HNG, for short.HNG solicits translations only for trigger n-gramsand not for entire sentences. We provide senten-tial context, highlight the trigger n-gram that wewant translated, and ask for...
... 2008. Optimizing Chinese word segmen-tation formachinetranslation performance. In Pro-ceedings of the Third Workshop on Statistical Ma-chine Translation, pages 224–232, Columbus, Ohio.David ... Oflazer. 2006.Initial explorations in English to Turkish statistical machine translation. In Proceedings of the Work-shop on StatisticalMachine Translation, pages 7–14, New York City, New York, ... 31–36,Uppsala, Sweden, 13 July 2010.c2010 Association for Computational LinguisticsUnsupervised Search for The Optimal Segmentation for Statistical Machine Translation Cos¸kun Mermer1,3and Ahmet Afs¸ın...
... Association for Computational Linguistics.Philipp Koehn et al. 2007. Moses: Open source toolkit for statisticalmachine translation. In Proceedings ofthe 45th Annual Meeting of the Association for ... Association for Computational Linguistics:shortpapers, pages 294–298,Portland, Oregon, June 19-24, 2011.c2011 Association for Computational LinguisticsCorpus Expansion forStatisticalMachineTranslation ... William Dolan. 2004.Monolingual machinetranslationfor paraphrase gen-eration. In Proceedings of EMNLP 2004, pages 142–149, Barcelona, Spain, July. Association for Computa-tional Linguistics.298...
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... smorgasbord of features for statisticalmachine translation. In Proceedings of HLT-NAACL 2004.Och, F. J., Tillmann, C., and Ney, H. (1999). Improved align-ment models forstatisticalmachine translation. ... mathematics of statisticalmachine translation. Computational Linguistics, 19(2):263–313.Charniak, E., Knight, K., and Yamada, K. (2003). Syntax-basedlanguage models forstatisticalmachine translation. ... P. (2004). Statistical significance tests for machine translation evaluation. In Lin, D. and Wu, D., editors, Pro-ceedings of EMNLP 2004.Koehn, P. and Knight, K. (2003). Feature- rich statistical...
... the feature vector of the -th instance, corresponding to la-bel . The symbol is short-hand for the feature- vector . This formulation is slightly differ-ent from the standard maximum entropy formulation ... @us.ibm.comAbstractIn this paper, we present a novel trainingmethod for a localized phrase-based predic-tion model forstatisticalmachine translation (SMT). The model predicts blocks with orien-tation ... block-based model for statis-tical machine translation. A block is a pair of phraseswhich are translations of each other. For example, Fig. 1shows an Arabic-English translation example that usesblocks....
... entropy approach is outlined inSection 3.2 StatisticalMachine Translation The goal of the translation process in statisti-cal machinetranslation can be formulated as fol-lows: A source language ... Preliminary translation results for theVerbmobil Test-147 for different contextual infor-mation and different thresholds using the top-10translations. The baseline translation results for model ... problem withinthe statistical framework is to use max-imum entropy methods. In this paper,we present how to use this type of in-formation within a statistical machine translation system....
... experimental results for a bilingual cor- pus are reported. 1.1 StatisticalMachineTranslation In statisticalmachine translation, the goal of the search strategy can be formulated as follows: ... about 3.5. 963 A DP based Search Algorithm forStatisticalMachineTranslation S. Nieflen, S. Vogel, H. Ney, and C. Tillmann Lehrstuhl fiir Informatik VI RWTH Aachen - University of Technology ... easy-to-use measure of the translation performance, the Levenshtein distance between the produced translations and the sample translations was calculated. The translation results are summarized...
... 2009.c2009 Association for Computational LinguisticsBilingually Motivated Domain-Adapted Word Segmentation for StatisticalMachine Translation Yanjun Ma Andy WayNational Centre for Language TechnologySchool ... observe that the ICT and Stanford segmenterconsistently outperform the LDC segmenter. Evenusing 3M sentence pairs for training, the differ-ences between them are still statistically signifi-cant ... LDC seg-menter2and Stanford segmenter version 2006-05-113. Both ICTCLAS and Stanford segmentersutilise machine learning techniques, with HiddenMarkov Models for ICT (Zhang et al., 2003)...
... smorgasbordof features forstatisticalmachine translation. In HLT-NAACL 2004: Main Proceedings, pages 161–168.F. J. Och. 2003. Minimum error rate training for statisti-cal machine translation. ... classifi-cation error training forstatisticalmachine translation. In EAMT.C. Wang, M. Collins, and P. Koehn. 2007. Chinese syn-tactic reordering forstatisticalmachine translation. InEMNLP, pages ... Kucerova. 2005. Clause re-structuring forstatisticalmachine translation. In ACL,pages 531–540.J. Eisner. 2003. Learning non-ismorphic tree mappings for machine translation. In ACL, Sapporo, Japan.Short...
... of features, lexi-cal features and collocation features. For a blockb = (s, t), we use s1to denote the first word of thesource s, t1to denote the first word of the target t.Lexical features ... during translation. This indicatesthat boundary words of blocks may keep informa-tion for their movements/reorderings. To test thishypothesis, we calculate the information gain ra-tio (IGR) for ... 521–528,Sydney, July 2006.c2006 Association for Computational LinguisticsMaximum Entropy Based Phrase ReorderingModel forStatisticalMachine Translation Deyi XiongInstitute of Computing...