'Source word' diaporamas de présentation

MLA GUIDELINES

MLA GUIDELINES. Your Research Tool. MLA Guideline Lesson. It is time to learn how to put all of your hard work into a paper and give proper credit!! Let us begin by navigating through the PowerPoint! This button will navigate you to the next slide.

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Getting the structure right for word alignment: LEAF

Getting the structure right for word alignment: LEAF. Alexander Fraser and Daniel Marcu Presenter Qin Gao. Quick summary. Problem. Result. IBM Models have 1-N assumption. Significant Improvement on BLEU (AR-EN). Solutions. A sophisticated generative story.

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(Statistical) Approaches to Word Alignment

(Statistical) Approaches to Word Alignment. 11-734 Advanced Machine Translation Seminar Sanjika Hewavitharana Language Technologies Institute Carnegie Mellon University 02/02/2006. Word Alignment Models. We want to learn how to translate words and phrases Can learn it from parallel corpora

458 views • 29 slides

(Statistical) Approaches to Word Alignment

(Statistical) Approaches to Word Alignment. 11-734 Advanced Machine Translation Seminar Sanjika Hewavitharana Language Technologies Institute Carnegie Mellon University 02/02/2006. Word Alignment Models. We want to learn how to translate words and phrases Can learn it from parallel corpora

293 views • 29 slides

Transliteration

Transliteration. CS 626 course seminar by Purva Joshi 08305907 Mugdha Bapat 07305916 Aditya Joshi 08305908 Manasi Bapat 08305906. Humans transliterate frequently for different reasons Can a machine do this? (Why would a machine have to do this?) If yes, how?.

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Posterior Regularization for Structured Latent Variable Models

Posterior Regularization for Structured Latent Variable Models. Li Z honghua I2R SMT Reading Group. Outline. Motivation and Introduction Posterior Regularization Application Implementation Some Related Frameworks. Motivation and Introduction. Prior Knowledge

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Extraction of Bilingual Information from Parallel Texts

Extraction of Bilingual Information from Parallel Texts. Mike Rosner. Outline. Machine Translation Traditional vs. Statistical Architectures Experimental Results Conclusions. Translational Equivalence: many:many relation. SOURCE. TARGET. Traditional Machine Translation. Remarks.

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Avoiding Plagiarism

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Avoiding Plagiarism

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Bayesian Word Alignment for Statistical Machine Translation

Bayesian Word Alignment for Statistical Machine Translation. Authors: Coskun Mermer, Murat Saraclar Present by Jun Lang 2011-10-13 I2R SMT-Reading Group. Paper info. Bayesian Word Alignment for Statistical Machine Translation ACL 2011 Short Paper With Source Code in Perl on 379 lines

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172 views • 9 slides