Ppt on machine translation

Error Analysis of Rule-based Machine Translation Outputs A Case Study on English – Persian MT System تجزیه و تحلیل خطا از حکومت مبتنی بر خروجی دستگاه ترجمه؟

analysis methods toward more macrotextual methods focused on the function, purpose and effect of the text. At the same time, machine translation assessment has mainly been microtextual and focused on the aspects of accuracy and fluency. purposecontext Hovy (2002) discussed the / because we have to place them on how they are realized rather than the cause of errors and many machine translated sentences contained multiple, linked errors. Future work Future work will therefore be focused on the cause of errors /


Stephan Vogel - Machine Translation1 Stephan Vogel Spring Semester 2011 Machine Translation Minimum Error Rate Training.

Stephan Vogel Spring Semester 2011 Machine Translation Minimum Error Rate Training Stephan Vogel - Machine Translation2 Overview lOptimization approaches lSimplex lMER lAvoiding local minima lAdditional considerations lTuning towards different metrics lTuning on different development sets Stephan Vogel - Machine Translation3 Tuning the SMT System lWe use different models in SMT system lModels have simplifications lTrained on different amounts of data l=> Models have different levels of/


Statistical Machine Translation Kevin Knight USC/Information Sciences Institute USC/Computer Science Department.

: How Much Data Do We Need? Amount of bilingual training data Quality of automatically trained machine translation system MT Evaluation Manual: –SSER (subjective sentence error rate) –Correct/Incorrect –Error categorization /EM training! For details, see: “A Statistical MT Tutorial Workbook” (Knight, 1999). “The Mathematics of Statistical Machine Translation” (Brown et al, 1993) Software: GIZA++ Statistical Machine Translation … la maison … la maison bleue … la fleur … … the house … the blue house … the /


July 11, 2010ACL 2010 Tutorial, Uppsala, Sweden1 Tree-based and Forest-based Translation Yang LiuLiang Huang Institute of Computing Technology Chinese.

Brown, Stephan A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2): 263-311. Sylvie Billot and Bernard Lang. 1989. The structure of shared forests/15(3): 465-488. Yang Liu, Qun Liu, and Shouxun Lin. 2006. Tree-to-string alignment template for statistical machine translation. In Proceedings of COLING-ACL 2006. July 11, 2010 ACL 2010 Tutorial, Uppsala, Sweden 127 Bibliography Yang Liu, /


VM && QEMU Date:2010/04/09, rednoah. Outline  Introduction to Virtual Machine  VM Overview  Interpretation  Binary Translation  Process VM  Introduction.

CC must be save/restored Case 2: Only source machine has CC  target machine must simulate the CC  some source machines set many CC in one instruction Case 3: Only target machine has CC  compare & branch is emulated by two instruction Case 4: Neither target nor source have CC  no issues Binary Translation  CC is set more often than referenced.  If a CC is set/


November 2004 J. E. Smith Virtual Machines: An Architecture Perspective.

. Smith 32 HLL VM Research  Metadata – an interesting concept Data Set Architecture Don’t have to discover data structures – compare with C programs. Metadata Code Machine Independent Program File Loader Virtual Machine Implementation Interpreter Internal Data Structures Translator Native Code VMs (c) 2004, J. E. Smith 33 HLL VM Research  Precise trap model Problems in conventional processors: All state precise Many instructions can/


PROGRAMMING 8.0. 8.1 Introduction To Programming Definition Types Of Programming Languages Programming Language Paradigm Translator 8.1.1 8.1.2 8.1.4.

in a different computer languages. 8.1.4 The Translator, cont’ Translator for Machine Language ? Case #1 : Do a translator need to translate a code written in machine language into a machine language ? Answer : NO translation needed. Translator for Assembly Language ? Case #2 : Do a translator need to translate a code written in assembly language into a machine language ? Answer : YES, translator use an Assembler Translator for High-level Language ? Case #3 : Do a/


Evaluation of Machine Translation Systems: Metrics and Methodology Alon Lavie Language Technologies Institute School of Computer Science Carnegie Mellon.

for MT Evaluation with High Levels of Correlation with Human Judgments". In Proceedings of the Second Workshop on Statistical Machine Translation at the 45th Meeting of the Association for Computational Linguistics (ACL-2007), Prague, Czech Republic, June 2007. / or HTER? Exploring Different Human Judgments with a Tunable MT Metric”, In Proceedings of the Fourth Workshop on Statistical Machine Translation at EACL-2009, Athens, Greece, March 2009. Pages 259-268. July 3, 2009Óbidos Workshop83 Questions? July/


1 Session 1 Advantages and Disadvantages of Translation Technology (TT) - Historical development of translation technology - Focus on TM and MT (Theory.

tools data-driven MT online term banks free WebMT (1997: Babelfish) web localisation services Translation Memory ICT Development TT Development 5 Translation Technology Continuum automation human involvement Automatic Translation Unaided Translation Computer-aided Translation (CAT) Translation process automated by use of Machine Translation Translation process aided by electronic tools such as Translation Memory Translation process not aided by any electronic tools Adapted from Hutchins & Somers (1992) 6/


Approved for Public Release, Distribution Unlimited Machine Translation at DARPA Joseph Olive Program Manager.

DARPA ●Four Decades of Research ●Continuous progress ●Limited vocabulary single talker ●Speaker-independent speech recognition ●Large vocabulary ●Machine translation ●Natural language processing ●TIDES and EARS ●Great Accomplishments ●Need for a New Program 2 Approved for Public /the city of penalty Baquba. 11 errors in 33 words (67% accuracy) Deletion Insertion Corrected machine translation Human-Translated Reference The statement said that “your brothers in the military wing of the Al-Qaeda Jihad /


CHAPTER 6 INTRODUCTION TO SYSTEM SOFTWARE AND VIRTUAL MACHINES.

must have a unique label HOW DIFFICULT IS IT TO WRITE THE ASSEMBLER--- the program which translates assembly language programs into machine language? The algorithm for the assembler is given on pages 260 and 262 of the text. Remember, the assembler/4 x:.data 8 z:.data 0.end y 5 x 6 z 7 Output: the object code (i.e. the machine code translation) 0000000000000110 0011000000000101 0001000000000111 0100000000000111 1111000000000000 0000 0000 0000 0100 0000 0000 0000 1000 0000 0000 SLIDES 16-28 ARE VERY, VERY IMPORTANT/


Recent Trends in MT Evaluation: Linguistic Information and Machine Learning Jason Adams 11-734 2008-03-05 Instructors: Alon Lavie Stephan Vogel.

to reference length  Word error rate minimum edit distance between hypothesis and any reference  Position-independent word error rate shorter translation removed from longer and size of remaining set returned Machine Learning: Kulesza & Shieber (2004) Trained an SVM using classification  Positive: human translation  Negative: machine translation Score is output of SVM  Distance to hyperplane is treated as a measure of confidence Classification Accuracy  ~59% for/


Christo Wilson Lecture 11: Virtual Machine Monitors

target multiple platforms E.g. OS X, Windows, and Linux Would you rather debug on three separate machines, or one machine with two guests? Technical Challenges x86 is not designed with virtualization in mind Some privileged instructions don’t /KVM on Linux Problem with AMD-V and VT-x Some operations are much slower when using vmexit vs. binary translation Guest OS Assembly Translated Assembly do_atomic_operation: cli mov eax, 1 … do_atomic_operation: call [vmm_disable_interrupts] mov eax, 1 … This code is /


Tree Automata for Automatic Language Translation kevin knight information sciences institute university of southern california.

of the World (of Automata for NLP) Weighted string automata in NLP – Applications transliteration machine translation language modeling speech, lexical processing, tagging, summarization, optical character recognition, … – Generic algorithms/in NLP – Applications – Generic algorithms and toolkits Some connections with theory Natural Language Transformations Machine Translation Name Transliteration Compression Question Answering Spelling Correction Speech Recognition Language Generation Text to Speech Input /


Introduction to Statistical Machine Translation Philipp Koehn Kevin Knight USC/Information Sciences Institute USC/Computer Science Department CSAIL Massachusetts.

capabilities in both interpretation and generation. People around the world stubbornly refuse to write everything in English. Machine Translation 美国关岛国际机场及其办公室均接获一 名自称沙地阿拉伯富商拉登等发出的电 子邮件,威胁将会向机场等公众地方发 动生化袭击後,关岛经保持高度戒备。 The U.S. island of Guam is maintaining a high state of alert after/ Osama bin Laden and threatening a biological/chemical attack against public places such as the airport. Machine translation: The American [?] international airport and its the office all receives one calls self the sand Arab/


MACHINE TRANSLATION TRANSLATION(5) LECTURE[1-1] Eman Baghlaf.

are certain circumstances when MT output maybe left unedited(as a raw translation)or only lightly corrected. Output may also server as a rough draft for human translators, as a pre-translation. Computerized Approaches Machine Translation (MT) Machine-Aided Human Translation (MAHT) Human-Aided Machine Translation (HAMT) Types of computerized approaches: Machine Translation (MT) Any system that actually performs a translation. Machine-Aided Human Translation (MAHT) They are systems which perform the task of/


Stephan Vogel - Machine Translation1 Machine Translation Factored Models Stephan Vogel Spring Semester 2011.

distinguished! lEg. for nouns: gender is irrelevant as is nominative, dative, and accusative; but genitive translates differently Sonja Nießen and Hermann Ney, Toward hierarchical models for statistical machine translation of inflected languages. Proceedings of the workshop on data-driven methods in machine translation - Volume 14, 2001. Stephan Vogel - Machine Translation12 Hierarchical Lexicon lEquivalence classes at different levels of abstraction lExample: ankommen ln is full analysis/


111 The Impact of Statistical Word Alignment Quality and Structure in Phrase-based Statistical Machine Translation A doctoral dissertation by: Francisco.

3 … and many languages …  4000-5000 different languages in the world  Access to information is limited by language barrier. 24 – November 2011Guzman 2011 4 … we need Machine Translation as a quick and cheap mean to perform translation. 24 – November 2011Guzman 2011 5 Pop quiz: what is she saying? Options  A) I had a big sandwich for lunch  B) I’ve had enough with/


January 26, 20001 Stochastic Transductions for Machine Translation* Giuseppe Riccardi AT&T Labs-Research *Joint work with Srinivas Bangalore and Enrico.

Press, Cambridge, Massachusetts. 1997 -S. Bangalore and G. Riccardi, "Stochastic Finite-State Models for Spoken Language Machine Translation", Workshop on Embedded Machine Translation Systems, NAACL, pp. 52-59, Seattle, May 2000. More references on http://www.research.att.com//Workshop, 2000) Here, lexical reordering as an FST. Integrated finite-state model for speech translation. 66 Application: How May I Help You? 67 Machine Translation “When I look at an article in Russian, I say: This is really written /


Statistical Machine Translation Or, How to Put a Lot of Work Into Getting the Wrong Answer Timothy White Presentation For CSC CSC 9010: Natural Language.

)http://www.isi.edu/natural- language/projects/rewrite/mtsummit03.pdf Dorr, B. and Monz, C. 2004. Statistical Machine Translation. Presented as lecture for CMSC 723 at the University of Maryland (available online at http://www.umiacs.umd.edu/~christof/.umd.edu/~christof/courses/cmsc723-fall04/lecture- notes/Lecture8-statmt.ppt Germann, U. 2003. Greedy Decoding for Statistical Machine Translation in Almost Linear Time. Proceedings of HLT-NAACL 2003. Edmonton, AB, Canada. (available online at http://acl.ldc/


Dependency Trees and Machine Translation Vamshi Ambati Spring 2008 Adv MT Seminar 02 April 2008.

Vamshi Ambati Vamshi@cs.cmu.edu Spring 2008 Adv MT Seminar 02 April 2008 Today Introduction –Dependency formalism –Syntax in Machine Translation Dependency Tree based Machine Translation –By projection –By synchronous modeling Conclusion and Future Today Introduction –Dependency formalism –Syntax in Machine Translation Dependency Tree based Machine Translation –By projection –By synchronous modeling Conclusion and Future Dependency Trees John gave Mary an apple Phrase Structure Trees Dependency Trees/


Machine Translation Dr. Radhika Mamidi. What is Machine Translation? A sub-field of computational linguistics It investigates the use of computer software.

in two languages from the parallel corpus for a given word, phrase or sentence. Given enough data, machine translation programs often work well enough for a native speaker of one language to get the approximate meaning of /of meaning between them. Types of MT systems 2. Statistical machine translation The machine translation tries to generate translations using statistical methods based on bilingual text corpora 3. Example-based machine translation The EBMT approach is often characterised by its use of a /


CSA405: Advanced Topics in NLP Machine Translation I Introduction to MT.

even mobile phones Between these two extremes there are other points of interest where technology can radically affect the productivity of the individual translator. Jan 2005CSA4050 MT I39 MAHT and HAMT Machine Aided Human Translation (MAHT) Human Aided Machine Translation (HAMT). The essential difference between these two lies not only in the way in which the person is involved but also in the extent of/


For language service providers. PROMT 20 years of successful development, advancement and implementation of text automatic translation solutions. 27 translation.

разъема на плате, но печатные платы скоро стали общепринятой практикой. Центральный процессор, память и периферийные устройства были размещены на отдельных печатных платах, которые устанавливались на объединительную плату. Examples of PROMT Machine Translation Translation direction: English Russian 15 Source text: Pneumologie Der Wandel der demographischen Struktur der Bevölkerung bedeutet vor allen Dingen einen drastischen Anstieg des Anteils älterer Menschen, einerseits infolge der gestiegenen/


Machine Translation Discriminative Word Alignment Stephan Vogel Spring Semester 2011.

Additional Dependencies lLog-linear model allows integration of additional dependencies, which contain additional information lPOS lParse trees l… lAdd additional variable to capture these dependencies lNew decision rule: Stephan Vogel - Machine Translation)5 Tasks lModeling: design feature functions which capture cross- lingual divergences lSearch: find alignment with highest probability lTraining: find optimal feature weights lMinimize alignment errors given some gold-standard alignments/


Week 9: resources for globalisation Finish spell checkers Machine Translation (MT) The ‘decoding’ paradigm Ambiguity Translation models Interlingua and.

ambiguity conceptual structure specificity ambiguity lexical gaps Machine translation Pragmatic approach Machine translation Pragmatic approach aim for a rough translation, ‘gist’ translation Used for multi-lingual information retrieval Machine translation Pragmatic approach aim for a rough translation, ‘gist’ translation Used for multi-lingual information retrieval involve human translators in the process: computer-aided translation Machine translation Translation models Transfer model ‘the dog bit my/


CSA405: Advanced Topics in NLP Machine Translation I Introduction to MT.

even mobile phones Between these two extremes there are other points of interest where technology can radically affect the productivity of the individual translator. Jan 2005CSA4050 MT I29 MAHT and HAMT Machine Aided Human Translation (MAHT) Human Aided Machine Translation (HAMT). The essential difference between these two lies not only in the way in which the person is involved but also in the extent of/


SYNTAX BASED MACHINE TRANSLATION UNDER GUIDANCE OF PROF PUSHPAK BHATTACHARYYA PRESENTED BY ROUVEN R Ӧ HRIG (10V05101) ERANKI KIRAN (10438004) SRIHARSA.

the fact of different placing in German and English David Chiang - A Hierarchical Phrase-Based Model for Statistical Machine Translation, Proceedings of the 43rd Annual Meeting of the ACL, Ann Arbor, June 2005. Synchronous grammar (1) / ACL, Ann Arbor, 21 June 2006. 4. Franz Josef Och and Hermann Ney - The alignment template approach to statistical machine translation, Computational Linguistics 2004 5. Zhifei Li, Chris Callison-Burch, Chris Dyer, Juri Ganitkevitch, Ann Irvine, Sanjeev Khudanpur, Lane /


Linguistics 187/287 Week 6 Martin Forst, Ron Kaplan, and Tracy King Generation Term-rewrite System Machine Translation.

purpose rewrite system (aka "transfer" or "xfr" system) Sample Uses of Rewrite System Sentence condensation Machine translation Mapping to logic for knowledge representation and reasoning Tutoring systems What does the system do? Input: set /rich (target) language particularly badly. How much manual labor involved in development of translation lexicon? Computationally expensive Grammatical Machine Translation Syntactic transfer-based approach Parsing and generation identical/similar between GMT I and GMT II/


Elements of Computing Systems, Nisan & Schocken, MIT Press, www.nand2tetris.org, Chapter 7: Virutal Machine, Part I slide 1www.nand2tetris.org Building.

if the current command is C_PUSH, C_POP, C_FUNCTION, or C_CALL. Elements of Computing Systems, Nisan & Schocken, MIT Press, www.nand2tetris.org, Chapter 7: Virutal Machine, Part I slide 33www.nand2tetris.org Proposed VM translator implementation: CodeWriter module CodeWriter: Translates VM commands into Hack assembly code. RoutineArgumentsReturnsFunction ConstructorOutput file / stream--Opens the output file/stream and gets ready to write into it. setFileName fileName/


Machine Translation- 2 Autumn 2008 Lecture 17 4 Sep 2008.

the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport. Machine translation: The American [?] international airport and its the office all receives one calls self the sand Arab rich business/the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport. Machine translation: The American [?] international airport and its the office all receives one calls self the sand Arab rich business/


Example-based Machine Translation Pursuing Fully Structural NLP Kurohashi-lab M1 56430 Toshiaki Nakazawa.

by A* algorithm Similarity between input and examples is calculated by word-based Edit Distance Need to reduce the # of calculations Outline I.History of Machine Translation II.Introduction of recent MT systems  Statistic Machine Translation (SMT)  Example-based Machine Translation (EBMT) III.Related work for EBMT  Logical Form  Efficient retrieval method IV.EBMT pursuing fully structural NLP V.Conclusion Why EBMT? Pursuing structural NLP –Improvement/


1 Gholamreza Haffari Simon Fraser University MT Summit, August 2009 Machine Learning approaches for dealing with Limited Bilingual Data in SMT.

Some slides are adapted or used from  Chris Callison Burch  Trevor Cohn  Dragos Stefan Munteanu 3 Statistical Machine TranslationTranslate from a source language to a target language by computer using a statistical model  M F  E is a/ Alignment”, ACL. 139 References  (Fraser & Marcu 2006b) A. Fraser and D. Marcu, “Measuring Word Alignment Quality for Statistical Machine Translation”, Technical Report ISI-TR-616, ISI/University of Southern California.  (Fung & Cheung 2004) P. Fung and P. Cheung, /


Jan 2005CSA4050 Machine Translation II1 CSA4050: Advanced Techniques in NLP Machine Translation II Direct MT Transfer MT Interlingual MT.

Advisory Committee) Report concludes that research funds should be directed into more fundamental linguistic research Jan 2005CSA4050 Machine Translation II3 History – Post ALPAC 1965-1970 –Operational Systems approach: SYSTRAN (eventually became the basis for / know  savoir know  connaître Not necessarily word for word schimmel  white horse Jan 2005CSA4050 Machine Translation II16 Transfer Model Degree of generalisation depends upon depth of representation: –Deeper the representation, harder it is/


Machine Translation Diana Trandab ă ţ Academic Year 2015-2016.

’ll get to that latter… What I expect you to know after today What is machine translation What is statistical machine translation Problems of machine translation What I expect you to know after today What is machine translation What is statistical machine translation Problems of machine translation We are not alone in the universe!? How do humans translate? Spend years learning a new language – memorizing words – learning syntactic patterns – exercising – … Use dictionaries and/


Bridging the Gap: Machine Translation for Lesser Resourced Languages Christian Monson, Ariadna Font Llitjós, Lori Levin, Alon Lavie, Alison Alvarez, Roberto.

) Syntactic Parsing Semantic Analysis Text Generation Source Language Target Language Interlingua Morphologial Analysis + Transfer Rule Based MT Direct Statistical MT Example Based MT +High quality -Expertise intensive development cycle 11 Machine Translation (MT) Syntactic Parsing Semantic Analysis Text Generation Source Language Target Language Interlingua Morphologial Analysis + Automate the development of deep-analysis MT +High quality -Expertise intensive development cycle 12 Our Position/


Generalitat de Catalunya Departament de la Vicepresidència Secretaria de Política Lingüística The machine translation service of the Generalitat of Catalonia.

of the Generalitat of Catalonia)  On-line translation of short texts  Website translation  On-line translation of documents The machine translation service of the Generalitat of Catalonia 4. Examples The machine translation service of the Generalitat of Catalonia 4. Examples The machine translation service of the Generalitat of Catalonia 4. Examples The machine translation service of the Generalitat of Catalonia 4. Examples The machine translation service of the Generalitat of Catalonia 4. Examples/


Introduction to Machine Translation CSC 5930 Machine Translation Fall 2014 Dr. Tom Way 1.

was an American scientist, mathematician, and science administrator. He is widely recognized as one of the pioneers of machine translation, and as an important figure in creating support for science in the United States. History of MT (3)/the… ALPAC (Automatic Language Processing Advisory Committee) report in 1966: “There is no immediate or predictable prospect of useful machine translation." History of MT (5) 7 The ALPAC (Automatic Language Processing Advisory Committee) was a govt. committee of seven /


Statistical Machine Translation

sentence f: “Maria no dio una bofetada a la bruja verde” Find the most likely English translation e: “Maria did not slap the green witch” Statistical Machine Translation Most likely English translation e is given by: P(e|f) estimates conditional probability of any e given f Statistical Machine Translation How to estimate P(e|f)? Noisy channel: Decompose P(e|f) into P(f|e/


Machine Translation activities at WIPO

description and claims only when requested by the user 65 languages supported using Google Translate! Quality of Google Translate improved for patent texts thanks to EPO sharing patent corpora with Google Search Results – machine translate Search Results – machine translate Search Results – machine translate Description – machine translate Description – machine translate Description – machine translate Description – machine translate 4. Development of in-house MT engines tuned for specific tasks In-house MT/


AMTA 2006Overview of Statistical MT1 An Overview of Statistical Machine Translation Charles Schafer David Smith Johns Hopkins University.

languages found on the web. Resource Availability Most of this tutorial AMTA 2006Overview of Statistical MT8 u Most statistical machine translation research has focused on a few high-resource languages (European, Chinese, Japanese, Arabic). ChineseArabic French ( ~/which is 14% exact-match correct * Or, we can put a correct translation in the top-10 list 34% of the time (useful for end-to-end machine translation or cross-language information retrieval) * Adding more bridge languages helps AMTA 2006Overview /


Virtual Machine Monitors

, but it isn’t Paravirtualization versus binary translation Hardware-assisted virtualization CPU Virtualization Basic technique: direct execution As long as it is executing unprivileged instructions the virtual machine (guest OS + applications) executes hardware instructions/the VMM isn’t notified. Two Ways to Handle Non-virtualizable Instructions Paravitualization Xen, Denali Binary Translation VMware Both use the same basic approach: catch non-virtualizable instructions and emulate them in software at/


GCSE Computing Lesson 5.

more secure than interpreted code. It produces an executable file so the program can be run without the source code. Disadvantages of using a compiler: It is a slow process translating the source code into machine code. Comparing a compiler and an interpreter An interpreter allows the programmer to run the source code but only within the interpreter. It does this by/


Chapter 16: Virtual Machines

support, mode CPU modes to improve virtualization performance Trap-and-Emulate Virtualization Implementation Building Block – Binary Translation Some CPUs don’t have clean separation between privileged and nonprivileged instructions Earlier Intel x86 CPUs are among/ to network (allowing direct access) And / or provide network address translation (NAT) NAT address local to machine on which guest is running, VMM provides address translation to guest to hide its address OS Component – Storage Management Both/


Machine Translation Introduction to MT. Dan Jurafsky Machine Translation Fully automatic Helping human translators Enter Source Text: Translation from.

from its name Dan Jurafsky MT in the modern age 1975-1985 Resurgence of MT in Europe and Japan Domain-specific rule-based systems 1990-present Rise of Statistical Machine Translation Machine Translation Introduction to MT Machine Translation Language Divergences Dan Jurafsky Language Similarities and Divergences Typology: the study of systematic cross-linguistic similarities and differences What are the dimensions along which human languages vary? Dan/


Machine Translation- 4 Autumn 2008 Lecture 19 10 Sep 2008.

the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport. Machine translation: The American [?] international airport and its the office all receives one calls self the sand Arab rich business/the Saudi Arabian Osama bin Laden and threatening a biological/chemical attack against public places such as the airport. Machine translation: The American [?] international airport and its the office all receives one calls self the sand Arab rich business/


Statistical Machine Translation SMT – Basic Ideas

Probs Probs Flat Structure EBMT SMT Deep Structure XFER, Interlingua Holy Grail Stephan Vogel - Machine Translation Statistical Machine Translation Translator translates source text Use machine learning techniques to extract useful knowledge Translation model: word and phrase translations Language model: how likely words follow in a particular sequence Translation system (decoder) uses these models to translates new sentences Advantages: Can quickly train for new languages Can adopt to new domains Problems/


Virtual Machine Monitors

, but it isn’t Virtualize memory Guest OS thinks it is managing memory directly, but it isn’t Paravirtualization versus binary translation Hardware-assisted virtualization CPU Virtualization Basic technique: direct execution As long as it is executing unprivileged instructions the virtual machine (guest OS + applications) executes hardware instructions directly. Note that in emulation direct execution isn’t possible since applications & the OS/


MT Evaluation: Human Measures and Assessment Methods 11-731: Machine Translation Alon Lavie February 23, 2011.

MT Evaluation: Human Measures and Assessment Methods 11-731: Machine Translation Alon Lavie February 23, 2011 11-731: Machine Translation2 Need for MT Evaluation MT Evaluation is important: –MT systems are / until recently Main Issues: definitions of scales, agreement, normalization across judges February 23, 201111-731: Machine Translation8 Human Ranking of MT Output Method: compare two or more translations of the same sentence and rank them in quality –More intuitive, less need to define exact criteria/


March 2005Intro to MT IV1 Postgraduate Diploma in Translation Introduction to Machine Translation IV The Translator’s Workstation.

-Driven MT Transfer Interlingua EBMT SMT March 2005 Intro to MT IV 3 Different Styles of MT FAMT: fully automatic machine translation  FAHQMT  FALQMT MAHT: machine aided human translation HAMT: human aided machine translation March 2005 Intro to MT IV 4 The Proper Place ofMen and Machines in Language Translation Martin Kay, 1980 [1997] Machine translation is an excellent research vehicle but stands no chance of filling actual needs for/


Ads by Google