'Mt systems' presentation slideshows

Machine Translation, Language Divergence and Lexical Resources

Machine Translation, Language Divergence and Lexical Resources. Pushpak Bhattacharyya Computer Science and Engineering Department IIT Bombay. Acknowledgement. NLP-AI members, CSE Dept, IIT Bombay. What is MT. Conversion of source language text to target language text. Computer Program.

710 views • 52 slides

CSA405: Advanced Topics in NLP

CSA405: Advanced Topics in NLP. Machine Translation I Introduction to MT. Outline. MT = Machine Translation Why MT is important What MT is and why MT is difficult MT and the Human Translator. Why Machine Translation is Important. Misconceptions about MT.

511 views • 51 slides

Stat-XFER: A General Framework for Search-based Syntax-driven MT

Stat-XFER: A General Framework for Search-based Syntax-driven MT. Alon Lavie Language Technologies Institute Carnegie Mellon University Joint work with: Erik Peterson, Alok Parlikar, Vamshi Ambati, Abhaya Agarwal, Greg Hanneman, Kevin Gimpel, Edmund Huber. Outline. Context and Rationale

467 views • 41 slides

Stat-XFER: A General Framework for Search-based Syntax-driven MT

Stat-XFER: A General Framework for Search-based Syntax-driven MT. Alon Lavie Language Technologies Institute Carnegie Mellon University Joint work with: Erik Peterson, Alok Parlikar, Vamshi Ambati, Abhaya Agarwal, Greg Hanneman, Kevin Gimpel, Edmund Huber. Outline. Context and Rationale

471 views • 41 slides

Improving Machine Translation Quality with Automatic Named Entity Recognition

Bogdan Babych Centre for Translation Studies University of Leeds, UK Department of Computer Science University of Sheffield, UK bogdan@comp.leeds.ac.uk. Anthony Hartley Centre for Translation Studies University of Leeds, UK a.hartley@leeds.ac.uk.

457 views • 31 slides

MT Evaluation: Human Measures and Assessment Methods

MT Evaluation: Human Measures and Assessment Methods. 11-731: Machine Translation Alon Lavie February 23, 2011. Need for MT Evaluation. MT Evaluation is important: MT systems are becoming wide-spread, embedded in more complex systems How well do they work in practice?

448 views • 27 slides

Taking on new challenges in multi-word unit processing for machine translation

Taking on new challenges in multi-word unit processing for machine translation. Johanna MONTI , Anabela BARREIRO, Annibale ELIA , Federica MARANO, Antonella NAPOLI. Outline. Multi-word units in the Lexicon-Grammar: definition.

349 views • 25 slides

Vamshi Ambati | Stephan Vogel | Jaime Carbonell Language Technologies Institute

A ctive Learning and C rowd-Sourcing for Machine T ranslation. Vamshi Ambati | Stephan Vogel | Jaime Carbonell Language Technologies Institute Carnegie Mellon University. Outline. Introduction Active Learning Crowd Sourcing Density-Based AL Methods Active Crowd Translation

357 views • 23 slides

Rationale for a multilingual corpus for machine translation evaluation

Rationale for a multilingual corpus for machine translation evaluation. Debbie Elliott Anthony Hartley Eric Atwell Corpus Linguistics 2003, Lancaster, England. Outline. Brief introduction to machine translation evaluation methods. Corpus content for MT evaluation by end-users.

357 views • 23 slides

Agenda

353 views • 23 slides

Montague Grammar and MT

Chris Brew, The Ohio State University http://www.purl.org/NET/cbrew.htm. Montague Grammar and MT. 1. Machine Translation and Montague Grammar. Great paper by Jan Landsbergen, in Readings in Machine Translation. The place of linguistics in MT What is the essence of Montague Grammar?

457 views • 21 slides

Machine Translation II

206 views • 20 slides

Modern MT Systems and the Myth of Human Translation: Real World Status Quo

Modern MT Systems and the Myth of Human Translation: Real World Status Quo. Intro MT & HT Definitions Comparison MT vs. HT Evaluation Methods FAE Framework Conclusion Discussion. Is This for Me?. (Freelance) translators and agencies Developers and vendors of MT systems

314 views • 18 slides

Cheshire at GeoCLEF 2008: Text and Fusion Approaches for GIR

Cheshire at GeoCLEF 2008: Text and Fusion Approaches for GIR. Ray R Larson School of Information University of California, Berkeley. Motivation.

228 views • 13 slides

Cross Language Information Retrieval (CLIR)

Cross Language Information Retrieval Based on Query Keyword Translation: An Internet Search Application. Atsushi Fujii and Tetsuya Ishikawa, International Journal of Computer Processing of Oriental Languages, 2000, 13:1, 1-13. Cross Language Information Retrieval (CLIR).

314 views • 13 slides

Language Technology in European Funding Programmes

243 views • 13 slides

Machine Translation

Machine Translation. History of Machine Translation Difficulties in Machine Translation Structure of Machine Translation System Research methods for Machine Translation. History of Machine Translation. Early 1950s Infancy time for Machine Translation

652 views • 13 slides

Machine Translation

Machine Translation. History of Machine Translation Difficulties in Machine Translation Structure of Machine Translation System Research methods for Machine Translation. History of Machine Translation. Early 1950s Infancy time for Machine Translation

526 views • 13 slides

On the Value of Machine Translation Adaptation

LREC Workshop: Automatic and Manual Metrics for Operational Translation Evaluation (MTE 2014) May 26, 2014. Approved for Public Release; Distribution Unlimited. 14-1556. On the Value of Machine Translation Adaptation. Stacey Bailey and Keith Miller. MT, DT, DTMT.

156 views • 9 slides

Multi-Engine Machine Translation

Multi-Engine Machine Translation. Stephan Vogel, Alon Lavie, Jaime Carbonell Bob Frederking, Ralf Brown Language Technologies Institute Carnegie Mellon University 5000 Forbes Av. Pittsburgh, PA 15213 Email: {vogel+, alavie, jgc}@cs.cmu.edu. MEMT Scenario.

160 views • 6 slides