PPT-Improving Statistical Machine Translation Accuracy

Using Bilingual Lexicon Extraction with Paraphrases 1 Chenhui Chu Toshiaki Nakazawa Sadao Kurohashi Graduate School of Informatics Kyoto University PACLIC2014 20141212 Statistical Machine . Costajuss57568 Marc Poch Adolfo Hern57569ndez and Jos57577 B Mari57585o TALP Research Centre Department of Signal Theory and Communications Universitat Polit57576cnica de Catalunya C Jordi Girona 13 08034 Barcelona Spain mfarrusmruizmpochadolfohhcan Post - System with a User Dictionary Masaki Itagaki , Takako Aikawa Microsoft One Microsoft Way, Redmond, WA 98052, U.S.A. ( mitagaki@microsoft.com, takakoa@microsoft.com ) A bstract A statistical ma Stefan . Riezler. Yi Liu. Google. 2010 Association for Computational Linguistics. Introduction. Create a system that learns to generate query rewrite from a large amount of user query logs.. Use query expansion in Web search for evaluation of rewritten queries.. By: Henry Zaremba. Origins of Translator Technology. 1954- IBM gives a demo of a translation program called the “Georgetown-IBM experiment”. 250 words in Russian; 6 grammatical rules. The database was a 16” disk. Introduction to MT. Machine Translation. Fully automatic. Helping human translators. Enter Source Text:. Translation from Stanford’s . Phrasal. :.  这 不过 是 一 个 时间 的 问题 . .. This is only a matter of time.. By: Henry Zaremba. Origins of Translator Technology. 1954- IBM gives a demo of a translation program called the “Georgetown-IBM experiment”. 250 words in Russian; 6 grammatical rules. The database was a 16” disk. Omid Kashefi. omid.Kashefi@pitt.edu. Visual Languages Seminar. November, 2016. Outline. Machine Translation. Deep Learning. Neural Machine Translation. Machine Translation. Machine Translation. Use of software in translating from one language into another. Gap. . between . Human and Machine Translation. Wu. . et. al., . arXiv. - . sept. 2016. Presenter. : Lütfi Kerem Şenel. Outline. . Introduction. . and. . Related. . w. orks. . Model Architecture . . by. . Jointly. Learning . to. . Align. . and. . Translate. Bahdanau. et. al., ICLR 2015. Presented. . by. İhsan Utlu. Outline. . Neural. Machine . Translation. . overview. Relevant. . V. . Gassenbauer. ,. J. Křivánek, K. . Bouatouch. ,. C. . Bouville. , M. . Ribardière. University of Rennes 1, France,. and. Charles University, Czech Republic. Need to compress large data set. SYNTAX BASED MACHINE TRANSLATION UNDER GUIDANCE OF PROF PUSHPAK BHATTACHARYYA PRESENTED BY ROUVEN R Ӧ HRIG (10V05101) ERANKI KIRAN (10438004) SRIHARSA MOHAPATRA (10405004) ARJUN ATREYA (09405011) 9/4/2011 Machine Translation. Fully automatic. Helping human translators. Enter Source Text:. Translation from Stanford’s . Phrasal. :.  这 不过 是 一 个 时间 的 问题 . .. This is only a matter of time.. to apply an ap-Our analysis with patience and helpful and getting led me me from 1950W ork Models Are Choosing a Conclusions and D A ei eto the score in language model model 693-2 Find the opti i. n Cross-lingual Question Answering. Kyoshiro Sugiyama. , Masahiro Mizukami, Graham Neubig, . Koichiro. Yoshino, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura. NAIST, Japan. Question answering (QA).