... mixture decoding (HM decoding) , a new decoding scheme that performs translation reconstruction using hypo-theses generated by multiple translation systems. HM decoding involves two decoding ... component systems. 4.6 Comparison to ConsensusDecoding Consensus decoding is another decoding technique that motivates our approach. We compare our HM decoding with two latest multiple -system ... component system, decoding methods based on multiple SMT systems can provide significant improvements on oracle translations; word-level system combination, collaborative decoding and model combination...
... classifi-cation error training forstatisticalmachine translation. In EAMT.C. Wang, M. Collins, and P. Koehn. 2007. Chinese syn-tactic reordering forstatisticalmachine translation. InEMNLP, pages ... 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. In ... 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...
... Disambiguation Improves StatisticalMachine Translation. In: Proceedings of ACL, Prague. D. Chiang. 2005. A hierarchical phrase-based model for statisticalmachine translation. In: Proceedings ... Phrase Selection for SMT. In: Goutte et al (ed.), Learning Machine Translation. MIT Press. K. Gimpel and N. A. Smith. 2008. Rich Source-Side Context forStatisticalMachine Translation. In: ... Association for Computational Linguistics, pages 834–843,Uppsala, Sweden, 11-16 July 2010.c2010 Association for Computational LinguisticsBilingual Sense Similarity forStatisticalMachine Translation...
... 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 ... source toolkit for parsing-based machine translation. In Proceedings of the FourthWorkshop on StatisticalMachine Translation, pages135–139, Athens, Greece, March. Association for Computational ... 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 ... abbreviations for systems are as follows:BL: Baseline system, GS: System trained with only gen-erated sentence pairs, IT: Interpolated phrase table withGS and BL,. GA and IA are GS and IT systems...
... for Phrase-based StatisticalMachineTranslation Mod-els. In Proc. of the Association forMachine Trans-lation in the Americas (AMTA).P. Koehn. 2004b. Statistical Significance Tests for MachineTranslation ... 2006.c2006 Association for Computational Linguistics Combination of Arabic Preprocessing Schemes for StatisticalMachine Translation Fatiha SadatInstitute for Information TechnologyNational ... Spain.Y. Lee. 2004. Morphological Analysis for Statistical Machine Translation. In Proc. of NAACL, Boston,MA.Y. Lee. 2005. IBM StatisticalMachineTranslation for Spoken Languages. In Proc. of International...
... reordered system produces a better translation for than the baselineIf baseline produces a better translation for than the reordered system. If the two systems produce equalquality translations ... 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. ...
... setting during decoding doesnot change performance: the list just restrictsthe possible target translations for a source phrase.Under this model, the log-probability of a possible translation ... pages 557–564,Ann Arbor, June 2005.c2005 Association for Computational LinguisticsA Localized Prediction Model forStatisticalMachine Translation Christoph Tillmann and Tong ZhangIBM T.J. ... @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...
... 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 ... 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. We show ... the lexicon models used in statistical machinetranslation systemsdo not include any kind of linguisticor contextual information, which oftenleads to problems in performing a cor-rect word...
... 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: ... 48.0 72.2 As far as we know, only two recent papers have dealt with decoding problem formachinetranslation systems that use translation models based on hid- den alignments without a monotonicity ... 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 ... and travel di-alogues. For the news domain, we trained our system using a portion of UN data for NIST2006 evaluation campaign. The system was de-veloped on LDC Multiple -Translation Chinese(MTC) ... (PB-SMT)1http://ictclas.org/index.html2http://www.ldc.upenn.edu/Projects/Chinese3http://nlp.stanford.edu/software/segmenter.shtml system Moses (Koehn et al., 2007). The perfor-mance of PB-SMT system is measured with BLEUscore (Papineni et...
... 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 ... possi-ble non-aligned word to get blocks E(b)6: for each block b∗∈ bE(b) do7: Register b∗to the links of four corners of it8: end for 9: end for 10: for each corner C in the matrix M do11:...