Statistical Machine Translation
|
|
|
- Harry Powers
- 8 years ago
- Views:
Transcription
1 Statistical Machine Translation What works and what does not Andreas Maletti Universität Stuttgart Stuttgart May 14, 2013 Statistical Machine Translation A. Maletti 1
2 Main notions Machine translation (MT) Automatic natural language translation as opposed to: manual translation computer-aided translation (by a computer) (e.g., translation memory) Statistical machine translation (SMT) MT using systems automatically obtained from (many) translations as opposed to: rule-based machine translation example-based machine translation (old) SYSTRAN translation by analogy Statistical Machine Translation A. Maletti 2
3 Main notions Machine translation (MT) Automatic natural language translation as opposed to: manual translation computer-aided translation (by a computer) (e.g., translation memory) Statistical machine translation (SMT) MT using systems automatically obtained from (many) translations as opposed to: rule-based machine translation example-based machine translation (old) SYSTRAN translation by analogy Statistical Machine Translation A. Maletti 2
4 Short history Timeline 1 Dark age (60s 90s) rule-based systems (e.g., SYSTRAN) CHOMSKYAN approach perfect translation, poor coverage 2 Reformation (1991 present) phrase-based and syntax-based systems statistical approach cheap, automatically trained 3 Potential future semantics-based systems (e.g., FRAMENET-based) semi-supervised, statistical approach basic understanding of translated text Statistical Machine Translation A. Maletti 3
5 Short history Timeline 1 Dark age (60s 90s) rule-based systems (e.g., SYSTRAN) CHOMSKYAN approach perfect translation, poor coverage 2 Reformation (1991 present) phrase-based and syntax-based systems statistical approach cheap, automatically trained 3 Potential future semantics-based systems (e.g., FRAMENET-based) semi-supervised, statistical approach basic understanding of translated text Statistical Machine Translation A. Maletti 3
6 Short history Timeline 1 Dark age (60s 90s) rule-based systems (e.g., SYSTRAN) CHOMSKYAN approach perfect translation, poor coverage 2 Reformation (1991 present) phrase-based and syntax-based systems statistical approach cheap, automatically trained 3 Potential future semantics-based systems (e.g., FRAMENET-based) semi-supervised, statistical approach basic understanding of translated text Statistical Machine Translation A. Maletti 3
7 Standard pipeline Schema Input Translation model Language model Output (the models are often integrated in practice) Required resources bilingual text (sentences in both languages) monolingual text (in target language) 1.5M sent. 44M sent. Statistical Machine Translation A. Maletti 4
8 Standard pipeline Schema Input Translation model Language model Output (the models are often integrated in practice) Required resources bilingual text (sentences in both languages) monolingual text (in target language) 1.5M sent. 44M sent. Statistical Machine Translation A. Maletti 4
9 Standard pipeline Schema Input Translation model Language model Output (the models are often integrated in practice) Required resources bilingual text (sentences in both languages) monolingual text (in target language) 1.5M sent. 44M sent. Statistical Machine Translation A. Maletti 4
10 Standard pipeline Example (Source: GOOGLE translate) Input: What works and what does not Segmentation: What works and what does not Translation model output: Was funktioniert Was am Was funktioniert am und was nicht und was nicht funktioniert und welche nicht ist und was nicht Statistical Machine Translation A. Maletti 5
11 Standard pipeline Example (Source: GOOGLE translate) Input: What works and what does not Segmentation: What works and what does not Translation model output: Was funktioniert Was am Was funktioniert am und was nicht und was nicht funktioniert und welche nicht ist und was nicht Statistical Machine Translation A. Maletti 5
12 Standard pipeline Example (Source: GOOGLE translate) Input: What works and what does not Segmentation: What works and what does not Translation model output: Was funktioniert Was am Was funktioniert am und was nicht und was nicht funktioniert und welche nicht ist und was nicht Statistical Machine Translation A. Maletti 5
13 Standard pipeline Example (Source: GOOGLE translate) Input: What works and what does not Segmentation: What works and what does not Translation model output: Was funktioniert Was am Was funktioniert am und was nicht und was nicht funktioniert und welche nicht ist und was nicht Statistical Machine Translation A. Maletti 5
14 Phrase-based machine translation And then the matter was decided, and everything was put in place f kan An tm AlHsm w wdet Almwr fy nsab ha Extracted information Segmentation: And then the matter was decided, and everything was put in place Phrase translation: Reordering: Statistical Machine Translation A. Maletti 6
15 Phrase-based machine translation And then the matter was decided, and everything was put in place f kan An tm AlHsm w wdet Almwr fy nsab ha Extracted information Segmentation: And then the matter was decided, and everything was put in place Phrase translation: Reordering: Statistical Machine Translation A. Maletti 6
16 Phrase-based machine translation And then the matter was decided, and everything was put in place f kan An tm AlHsm w wdet Almwr fy nsab ha Extracted information Segmentation: And then 1 the matter 2 was decided 3, and everything 4 was put 5 in place 6 Phrase translation: Reordering: Statistical Machine Translation A. Maletti 6
17 Phrase-based machine translation And then the matter was decided, and everything was put in place f kan An tm AlHsm w wdet Almwr fy nsab ha Extracted information Segmentation: And then 1 the matter 2 was decided 3, and everything 4 was put 5 in place 6 Phrase translation: f kan 1 Almwr 2 An tm AlHsm 3 w 4 wdet 5 fy nsab ha 6 Reordering: Statistical Machine Translation A. Maletti 6
18 Phrase-based machine translation And then the matter was decided, and everything was put in place f kan An tm AlHsm w wdet Almwr fy nsab ha Extracted information Segmentation: And then 1 the matter 2 was decided 3, and everything 4 was put 5 in place 6 Phrase translation: f kan 1 Almwr 2 An tm AlHsm 3 w 4 wdet 5 fy nsab ha 6 Reordering: ( ) Statistical Machine Translation A. Maletti 6
19 How it works Technical talks Marion Weller Daniel Quernheim and Nina Seemann phrase-based MT syntax-based MT Statistical Machine Translation A. Maletti 7
20 Small players Research at IMS Phrase-based MT (head: Dr. Alexander Fraser) Fabienne Braune Fabienne Cap Anita Ramm Marion Weller Syntax-based MT (head: Dr. Andreas Maletti) Fabienne Braune Daniel Quernheim Nina Seemann Statistical Machine Translation A. Maletti 8
21 Small players Research at IMS Phrase-based MT (head: Dr. Alexander Fraser) Fabienne Braune Fabienne Cap Anita Ramm Marion Weller Syntax-based MT (head: Dr. Andreas Maletti) Fabienne Braune Daniel Quernheim Nina Seemann Statistical Machine Translation A. Maletti 8
22 Small players Research at IMS Phrase-based MT (head: Dr. Alexander Fraser) Fabienne Braune Fabienne Cap Anita Ramm Marion Weller Syntax-based MT (head: Dr. Andreas Maletti) Fabienne Braune Daniel Quernheim Nina Seemann Statistical Machine Translation A. Maletti 8
23 Big players Commercial systems Language Studio GOOGLE translate WebSphere Translation Server BING translator OMNIFLUENT... Statistical Machine Translation A. Maletti 9
24 Big players Commercial systems Language Studio GOOGLE translate WebSphere Translation Server BING translator OMNIFLUENT... Soon also Statistical Machine Translation A. Maletti 9
25 Failures Statistical Machine Translation A. Maletti 10
26 Failures Applications Technical manuals Example (An mp3 player) The synchronous manifestation of lyrics is a procedure for can broadcasting the music, waiting the mp3 file at the same time showing the lyrics. With the this kind method that the equipments that synchronous function of support up broadcast to make use of document create setup, you can pass the LCD window way the check at the document contents that broadcast. That procedure returns offerings to have to modify, and delete, and stick top, keep etc. edit function. Statistical Machine Translation A. Maletti 11
27 Failures Applications Technical manuals Example (An mp3 player) The synchronous manifestation of lyrics is a procedure for can broadcasting the music, waiting the mp3 file at the same time showing the lyrics. With the this kind method that the equipments that synchronous function of support up broadcast to make use of document create setup, you can pass the LCD window way the check at the document contents that broadcast. That procedure returns offerings to have to modify, and delete, and stick top, keep etc. edit function. Statistical Machine Translation A. Maletti 11
28 Failures Applications Technical manuals Example (An mp3 player) The synchronous manifestation of lyrics is a procedure for can broadcasting the music, waiting the mp3 file at the same time showing the lyrics. With the this kind method that the equipments that synchronous function of support up broadcast to make use of document create setup, you can pass the LCD window way the check at the document contents that broadcast. That procedure returns offerings to have to modify, and delete, and stick top, keep etc. edit function. Statistical Machine Translation A. Maletti 11
29 Failures Applications Technical manuals Example (Hotel Uppsala, Sweden) Wir hatten die Zimmer eingestuft wird als Superior weil sie renoviert wurde im letzten Jahr oder zwei. Unsere Zimmer hatten Parkettboden und waren sehr geräumig. Man musste allerdings nicht musste seitwärts bewegen. Statistical Machine Translation A. Maletti 11
30 Failures Applications Technical manuals Example (Hotel Uppsala, Sweden) Wir hatten die Zimmer eingestuft wird als Superior weil sie renoviert wurde im letzten Jahr oder zwei. Unsere Zimmer hatten Parkettboden und waren sehr geräumig. Man musste allerdings nicht musste seitwärts bewegen. We stayed in rooms classified as superior because they had been renovated in the last year or two. Our rooms had wood floors and were roomy. You didn t have to walk sideways to move around. Statistical Machine Translation A. Maletti 11
31 Failures Applications Technical manuals US military Example (JONES, SHEN, HERZOG 2009) Soldier: Okay, what is your name? Local: Abdul. Soldier: And your last name? Local: Al Farran. Statistical Machine Translation A. Maletti 11
32 Failures Applications Technical manuals US military Example (JONES, SHEN, HERZOG 2009) Soldier: Okay, what is your name? Local: Abdul. Soldier: And your last name? Local: Al Farran. Speech-to-text machine translation Soldier: Local: Okay, what s your name? milk a mechanic and I am here I mean yes Statistical Machine Translation A. Maletti 11
33 Failures Applications Technical manuals US military Example (JONES, SHEN, HERZOG 2009) Soldier: Okay, what is your name? Local: Abdul. Soldier: And your last name? Local: Al Farran. Speech-to-text machine translation Soldier: Local: Soldier: Local: Okay, what s your name? milk a mechanic and I am here I mean yes What is your last name? every two weeks my son s name is ismail Statistical Machine Translation A. Maletti 11
34 Failures Applications Technical manuals US military MSDN, Knowledge Base... Statistical Machine Translation A. Maletti 11
35 But in many cases it actually works... Statistical Machine Translation A. Maletti 12
36 Selected application Lecture translation real-time speech-to-text machine translation combines automatic speech recognition and SMT requires lecturer training and terminology training automatically provides subtitles to lecture video Video Statistical Machine Translation A. Maletti 13
37 Selected application Lecture translation real-time speech-to-text machine translation combines automatic speech recognition and SMT requires lecturer training and terminology training automatically provides subtitles to lecture video Video Statistical Machine Translation A. Maletti 13
38 Summary SMT works well between similar languages (e.g., Spanish-English) between large resource languages (e.g., French-English) in-domain (training and test from the same domain) access to foreign language SMT could be better into morphologically rich / free word order languages (e.g., German) handling noisy inputs (e.g., chats, Twitter feeds) dealing with documents (instead of sentences) precision / translation accuracy Conclusion SMT is a cheap way to access foreign material Statistical Machine Translation A. Maletti 14
39 Summary SMT works well between similar languages (e.g., Spanish-English) between large resource languages (e.g., French-English) in-domain (training and test from the same domain) access to foreign language SMT could be better into morphologically rich / free word order languages (e.g., German) handling noisy inputs (e.g., chats, Twitter feeds) dealing with documents (instead of sentences) precision / translation accuracy Conclusion SMT is a cheap way to access foreign material Statistical Machine Translation A. Maletti 14
40 Summary SMT works well between similar languages (e.g., Spanish-English) between large resource languages (e.g., French-English) in-domain (training and test from the same domain) access to foreign language SMT could be better into morphologically rich / free word order languages (e.g., German) handling noisy inputs (e.g., chats, Twitter feeds) dealing with documents (instead of sentences) precision / translation accuracy Conclusion SMT is a cheap way to access foreign material Statistical Machine Translation A. Maletti 14
41 Summary SMT works well between similar languages (e.g., Spanish-English) between large resource languages (e.g., French-English) in-domain (training and test from the same domain) access to foreign language SMT could be better into morphologically rich / free word order languages (e.g., German) handling noisy inputs (e.g., chats, Twitter feeds) dealing with documents (instead of sentences) precision / translation accuracy Conclusion SMT is a cheap way to access foreign material Statistical Machine Translation A. Maletti 14
42 Summary SMT works well between similar languages (e.g., Spanish-English) between large resource languages (e.g., French-English) in-domain (training and test from the same domain) access to foreign language SMT could be better into morphologically rich / free word order languages (e.g., German) handling noisy inputs (e.g., chats, Twitter feeds) dealing with documents (instead of sentences) precision / translation accuracy Conclusion SMT is a cheap way to access foreign material Statistical Machine Translation A. Maletti 14
Computer Aided Translation
Computer Aided Translation Philipp Koehn 30 April 2015 Why Machine Translation? 1 Assimilation reader initiates translation, wants to know content user is tolerant of inferior quality focus of majority
Automatic Speech Recognition and Hybrid Machine Translation for High-Quality Closed-Captioning and Subtitling for Video Broadcast
Automatic Speech Recognition and Hybrid Machine Translation for High-Quality Closed-Captioning and Subtitling for Video Broadcast Hassan Sawaf Science Applications International Corporation (SAIC) 7990
Speech Processing Applications in Quaero
Speech Processing Applications in Quaero Sebastian Stüker www.kit.edu 04.08 Introduction! Quaero is an innovative, French program addressing multimedia content! Speech technologies are part of the Quaero
Statistical Machine Translation
Statistical Machine Translation Some of the content of this lecture is taken from previous lectures and presentations given by Philipp Koehn and Andy Way. Dr. Jennifer Foster National Centre for Language
A Joint Sequence Translation Model with Integrated Reordering
A Joint Sequence Translation Model with Integrated Reordering Nadir Durrani, Helmut Schmid and Alexander Fraser Institute for Natural Language Processing University of Stuttgart Introduction Generation
ACCURAT Analysis and Evaluation of Comparable Corpora for Under Resourced Areas of Machine Translation www.accurat-project.eu Project no.
ACCURAT Analysis and Evaluation of Comparable Corpora for Under Resourced Areas of Machine Translation www.accurat-project.eu Project no. 248347 Deliverable D5.4 Report on requirements, implementation
Overview of MT techniques. Malek Boualem (FT)
Overview of MT techniques Malek Boualem (FT) This section presents an standard overview of general aspects related to machine translation with a description of different techniques: bilingual, transfer,
HIERARCHICAL HYBRID TRANSLATION BETWEEN ENGLISH AND GERMAN
HIERARCHICAL HYBRID TRANSLATION BETWEEN ENGLISH AND GERMAN Yu Chen, Andreas Eisele DFKI GmbH, Saarbrücken, Germany May 28, 2010 OUTLINE INTRODUCTION ARCHITECTURE EXPERIMENTS CONCLUSION SMT VS. RBMT [K.
Introduction. Philipp Koehn. 28 January 2016
Introduction Philipp Koehn 28 January 2016 Administrativa 1 Class web site: http://www.mt-class.org/jhu/ Tuesdays and Thursdays, 1:30-2:45, Hodson 313 Instructor: Philipp Koehn (with help from Matt Post)
SYSTRAN Chinese-English and English-Chinese Hybrid Machine Translation Systems for CWMT2011 SYSTRAN 混 合 策 略 汉 英 和 英 汉 机 器 翻 译 系 CWMT2011 技 术 报 告
SYSTRAN Chinese-English and English-Chinese Hybrid Machine Translation Systems for CWMT2011 Jin Yang and Satoshi Enoue SYSTRAN Software, Inc. 4444 Eastgate Mall, Suite 310 San Diego, CA 92121, USA E-mail:
Hybrid Strategies. for better products and shorter time-to-market
Hybrid Strategies for better products and shorter time-to-market Background Manufacturer of language technology software & services Spin-off of the research center of Germany/Heidelberg Founded in 1999,
Semantics in Statistical Machine Translation
Semantics in Statistical Machine Translation Mihael Arcan DERI, NUI Galway firstname.lastname@deri.org Copyright 2011. All rights reserved. Overview 1. Statistical Machine Translation (SMT) 2. Translations
Chapter 5. Phrase-based models. Statistical Machine Translation
Chapter 5 Phrase-based models Statistical Machine Translation Motivation Word-Based Models translate words as atomic units Phrase-Based Models translate phrases as atomic units Advantages: many-to-many
The history of machine translation in a nutshell
1. Before the computer The history of machine translation in a nutshell 2. The pioneers, 1947-1954 John Hutchins [revised January 2014] It is possible to trace ideas about mechanizing translation processes
Statistical Machine Translation Lecture 4. Beyond IBM Model 1 to Phrase-Based Models
p. Statistical Machine Translation Lecture 4 Beyond IBM Model 1 to Phrase-Based Models Stephen Clark based on slides by Philipp Koehn p. Model 2 p Introduces more realistic assumption for the alignment
Question template for interviews
Question template for interviews This interview template creates a framework for the interviews. The template should not be considered too restrictive. If an interview reveals information not covered by
Integration of Content Optimization Software into the Machine Translation Workflow. Ben Gottesman Acrolinx
Integration of Content Optimization Software into the Machine Translation Workflow Ben Gottesman Acrolinx What is Acrolinx? Acrolinx is Content Optimization Software. It helps authors make their text!
SYSTRAN 混 合 策 略 汉 英 和 英 汉 机 器 翻 译 系 统
SYSTRAN Chinese-English and English-Chinese Hybrid Machine Translation Systems Jin Yang, Satoshi Enoue Jean Senellart, Tristan Croiset SYSTRAN Software, Inc. SYSTRAN SA 9333 Genesee Ave. Suite PL1 La Grande
Phrase-Based MT. Machine Translation Lecture 7. Instructor: Chris Callison-Burch TAs: Mitchell Stern, Justin Chiu. Website: mt-class.
Phrase-Based MT Machine Translation Lecture 7 Instructor: Chris Callison-Burch TAs: Mitchell Stern, Justin Chiu Website: mt-class.org/penn Translational Equivalence Er hat die Prüfung bestanden, jedoch
Extracting translation relations for humanreadable dictionaries from bilingual text
Extracting translation relations for humanreadable dictionaries from bilingual text Overview 1. Company 2. Translate pro 12.1 and AutoLearn 3. Translation workflow 4. Extraction method 5. Extended
Machine Translation at the European Commission
Directorate-General for Translation Machine Translation at the European Commission Konferenz 10 Jahre Verbmobil Saarbrücken, 16. November 2010 Andreas Eisele Project Manager Machine Translation, ICT Unit
The XMU Phrase-Based Statistical Machine Translation System for IWSLT 2006
The XMU Phrase-Based Statistical Machine Translation System for IWSLT 2006 Yidong Chen, Xiaodong Shi Institute of Artificial Intelligence Xiamen University P. R. China November 28, 2006 - Kyoto 13:46 1
Project Management. From industrial perspective. A. Helle M. Herranz. EXPERT Summer School, 2014. Pangeanic - BI-Europe
Project Management From industrial perspective A. Helle M. Herranz Pangeanic - BI-Europe EXPERT Summer School, 2014 Outline 1 Introduction 2 3 Translation project management without MT Translation project
Convergence of Translation Memory and Statistical Machine Translation
Convergence of Translation Memory and Statistical Machine Translation Philipp Koehn and Jean Senellart 4 November 2010 Progress in Translation Automation 1 Translation Memory (TM) translators store past
C E D A T 8 5. Innovating services and technologies for speech content management
C E D A T 8 5 Innovating services and technologies for speech content management Company profile 25 years experience in the market of transcription/reporting services; Cedat 85 Group: Cedat 85 srl Subtitle
Teacher education and its internationalisation LATVIA
Teacher education and its internationalisation LATVIA Dr.Sandra Kraze BA School of Business and Finance, international expert H EReform Experts in the STEERING project Bonn, February 11, 2015 Outline Overview
Customizing an English-Korean Machine Translation System for Patent Translation *
Customizing an English-Korean Machine Translation System for Patent Translation * Sung-Kwon Choi, Young-Gil Kim Natural Language Processing Team, Electronics and Telecommunications Research Institute,
Getting Off to a Good Start: Best Practices for Terminology
Getting Off to a Good Start: Best Practices for Terminology Technologies for term bases, term extraction and term checks Angelika Zerfass, zerfass@zaac.de Tools in the Terminology Life Cycle Extraction
LANGUAGE TRANSLATION SOFTWARE EXECUTIVE SUMMARY Language Translation Software Market Shares and Market Forecasts Language Translation Software Market
LANGUAGE TRANSLATION SOFTWARE EXECUTIVE SUMMARY Language Translation Software Market Shares and Market Forecasts Language Translation Software Market Driving Forces Language Translation Software Cloud
Survey Results: Requirements and Use Cases for Linguistic Linked Data
Survey Results: Requirements and Use Cases for Linguistic Linked Data 1 Introduction This survey was conducted by the FP7 Project LIDER (http://www.lider-project.eu/) as input into the W3C Community Group
Neural Machine Transla/on for Spoken Language Domains. Thang Luong IWSLT 2015 (Joint work with Chris Manning)
Neural Machine Transla/on for Spoken Language Domains Thang Luong IWSLT 2015 (Joint work with Chris Manning) Neural Machine Transla/on (NMT) End- to- end neural approach to MT: Simple and coherent. Achieved
The TCH Machine Translation System for IWSLT 2008
The TCH Machine Translation System for IWSLT 2008 Haifeng Wang, Hua Wu, Xiaoguang Hu, Zhanyi Liu, Jianfeng Li, Dengjun Ren, Zhengyu Niu Toshiba (China) Research and Development Center 5/F., Tower W2, Oriental
The KIT Translation system for IWSLT 2010
The KIT Translation system for IWSLT 2010 Jan Niehues 1, Mohammed Mediani 1, Teresa Herrmann 1, Michael Heck 2, Christian Herff 2, Alex Waibel 1 Institute of Anthropomatics KIT - Karlsruhe Institute of
Anatomy of Social Media Translation Gene Schriver
Anatomy of Social Media Translation Gene Schriver Ground Rules You may not learn anything new about the process of translation. This is a discussion, not a speech. If you do not participate, I will do
Machine vs. Human Translation Scott Bass, Advanced Language Translation Inc.
Machine vs. Human Translation Scott Bass, Advanced Language Translation Inc. Using computers to translate text from one language to another (referred to as machine translation [MT]) no longer faces the
NAPCS Product List for NAICS 54193: Translation and Interpretation Services
54193 1 Translation and interpretation services Converting written text, speech, or other live communication from one language to another. conversion to or from sign language or Braille. terminology and
M LTO Multilingual On-Line Translation
O non multa, sed multum M LTO Multilingual On-Line Translation MOLTO Consortium FP7-247914 Project summary MOLTO s goal is to develop a set of tools for translating texts between multiple languages in
Hybrid Machine Translation Guided by a Rule Based System
Hybrid Machine Translation Guided by a Rule Based System Cristina España-Bonet, Gorka Labaka, Arantza Díaz de Ilarraza, Lluís Màrquez Kepa Sarasola Universitat Politècnica de Catalunya University of the
Segmentation and Punctuation Prediction in Speech Language Translation Using a Monolingual Translation System
Segmentation and Punctuation Prediction in Speech Language Translation Using a Monolingual Translation System Eunah Cho, Jan Niehues and Alex Waibel International Center for Advanced Communication Technologies
Creating Captions in YouTube
Creating Captions in YouTube You can add your own captions to your videos. First, you will need a Google account where you can upload your videos to YouTube. You do not have to leave your videos there
Why Evaluation? Machine Translation. Evaluation. Evaluation Metrics. Ten Translations of a Chinese Sentence. How good is a given system?
Why Evaluation? How good is a given system? Machine Translation Evaluation Which one is the best system for our purpose? How much did we improve our system? How can we tune our system to become better?
Machine Translation. Why Evaluation? Evaluation. Ten Translations of a Chinese Sentence. Evaluation Metrics. But MT evaluation is a di cult problem!
Why Evaluation? How good is a given system? Which one is the best system for our purpose? How much did we improve our system? How can we tune our system to become better? But MT evaluation is a di cult
Translation Solution for
Translation Solution for Case Study Contents PROMT Translation Solution for PayPal Case Study 1 Contents 1 Summary 1 Background for Using MT at PayPal 1 PayPal s Initial Requirements for MT Vendor 2 Business
The Influence of Topic and Domain Specific Words on WER
The Influence of Topic and Domain Specific Words on WER And Can We Get the User in to Correct Them? Sebastian Stüker KIT Universität des Landes Baden-Württemberg und nationales Forschungszentrum in der
Multilingual Term Extraction as a Service from Acrolinx. Ben Gottesman Michael Klemme Acrolinx CHAT2013
Multilingual Term Extraction as a Service from Acrolinx Ben Gottesman Michael Klemme Acrolinx CHAT2013 Definitions term extraction: automatically identifying potential terms in a document (corpus) multilingual
Chapter 6. Decoding. Statistical Machine Translation
Chapter 6 Decoding Statistical Machine Translation Decoding We have a mathematical model for translation p(e f) Task of decoding: find the translation e best with highest probability Two types of error
The Transition of Phrase based to Factored based Translation for Tamil language in SMT Systems
The Transition of Phrase based to Factored based Translation for Tamil language in SMT Systems Dr. Ananthi Sheshasaayee 1, Angela Deepa. V.R 2 1 Research Supervisior, Department of Computer Science & Application,
The SYSTRAN Linguistics Platform: A Software Solution to Manage Multilingual Corporate Knowledge
The SYSTRAN Linguistics Platform: A Software Solution to Manage Multilingual Corporate Knowledge White Paper October 2002 I. Translation and Localization New Challenges Businesses are beginning to encounter
Empirical Machine Translation and its Evaluation
Empirical Machine Translation and its Evaluation EAMT Best Thesis Award 2008 Jesús Giménez (Advisor, Lluís Màrquez) Universitat Politècnica de Catalunya May 28, 2010 Empirical Machine Translation Empirical
WHITE PAPER. Machine Translation of Language for Safety Information Sharing Systems
WHITE PAPER Machine Translation of Language for Safety Information Sharing Systems September 2004 Disclaimers; Non-Endorsement All data and information in this document are provided as is, without any
RemoSync Business Email (Brew MP)
RemoSync Business Email (Brew MP) (Version 1.0) Setup Guide You must download and subscribe to the RemoSync client before you can begin using the application. You may also need to contact your Microsoft
Your single-source partner for corporate product communication. Transit NXT Evolution. from Service Pack 0 to Service Pack 8
Transit NXT Evolution from Service Pack 0 to Service Pack 8 April 2009: Transit NXT Service Pack 0 (Version 4.0.0.671) Additional versions of DTP programs supported: InDesign CS3 and FrameMaker 9 Additional
Translation and Localization Services
Translation and Localization Services Company Overview InterSol, Inc., a California corporation founded in 1996, provides clients with international language solutions. InterSol delivers multilingual solutions
Glossary of translation tool types
Glossary of translation tool types Tool type Description French equivalent Active terminology recognition tools Bilingual concordancers Active terminology recognition (ATR) tools automatically analyze
Adaptation to Hungarian, Swedish, and Spanish
www.kconnect.eu Adaptation to Hungarian, Swedish, and Spanish Deliverable number D1.4 Dissemination level Public Delivery date 31 January 2016 Status Author(s) Final Jindřich Libovický, Aleš Tamchyna,
CINDOR Conceptual Interlingua Document Retrieval: TREC-8 Evaluation.
CINDOR Conceptual Interlingua Document Retrieval: TREC-8 Evaluation. Miguel Ruiz, Anne Diekema, Páraic Sheridan MNIS-TextWise Labs Dey Centennial Plaza 401 South Salina Street Syracuse, NY 13202 Abstract:
Applications of speech-to-text in customer service. Dr. Joachim Stegmann Deutsche Telekom AG, Laboratories
Applications of speech-to-text in customer service. Dr. Joachim Stegmann Deutsche Telekom AG, Laboratories Contents. 1. Motivation 2. Scenarios 2.1 Voice box / call-back 2.2 Quality management 3. Technology
www.sdl.com SDL Trados Studio 2015 Translation Memory Management Quick Start Guide
www.sdl.com SDL Trados Studio 2015 Translation Memory Management Quick Start Guide SDL Trados Studio 2015 Translation Memory Management Quick Start Guide Copyright Information Copyright 2011-2015 SDL Group.
Machine Translation as a translator's tool. Oleg Vigodsky Argonaut Ltd. (Translation Agency)
Machine Translation as a translator's tool Oleg Vigodsky Argonaut Ltd. (Translation Agency) About Argonaut Ltd. Documentation translation (Telecom + IT) from English into Russian, since 1992 Main customers:
MLLP Transcription and Translation Platform
MLLP Transcription and Translation Platform Alejandro Pérez González de Martos, Joan Albert Silvestre-Cerdà, Juan Daniel Valor Miró, Jorge Civera, and Alfons Juan Universitat Politècnica de València, Camino
Central Release and Build Management with TFS. Christian Schlag
Central Release and Build Management with TFS Christian Schlag OUR DAILY MOTIVATION It s hard enough for software developers to write code that works on their machine. But even when it s done, there s
Comprendium Translator System Overview
Comprendium System Overview May 2004 Table of Contents 1. INTRODUCTION...3 2. WHAT IS MACHINE TRANSLATION?...3 3. THE COMPRENDIUM MACHINE TRANSLATION TECHNOLOGY...4 3.1 THE BEST MT TECHNOLOGY IN THE MARKET...4
REALIZATION SORTING ALGORITHM USING PARALLEL TECHNOLOGIES bachelor, Mikhelev Vladimir candidate of Science, prof., Sinyuk Vasily
2. В. Гергель: Современные языки и технологии параллельного программирования. М., Изд-во МГУ, 2012. 3. Синюк, В. Г. Алгоритмы и структуры данных. Белгород: Изд-во БГТУ им. В. Г. Шухова, 2013. REALIZATION
www.sdl.com SDL Trados Studio 2015 Project Management Quick Start Guide
www.sdl.com SDL Trados Studio 2015 Project Management Quick Start Guide SDL Trados Studio 2015 Project Management Quick Start Guide Copyright Information Copyright 2011-2015 SDL Group. Nothing contained
PERFECTION IN CREATIVITY
Poor subtitling is the quickest way to waste the hard work and money invested in your audiovisual production. Our team of subtitling and translation experts will provide you with synchronized subtitle
COPYRIGHT 2011 COPYRIGHT 2012 AXON DIGITAL DESIGN B.V. ALL RIGHTS RESERVED
Subtitle insertion GEP100 - HEP100 Inserting 3Gb/s, HD, subtitles SD embedded and Teletext domain with the Dolby HSI20 E to module PCM decoder with audio shuffler A A application product note COPYRIGHT
THUTR: A Translation Retrieval System
THUTR: A Translation Retrieval System Chunyang Liu, Qi Liu, Yang Liu, and Maosong Sun Department of Computer Science and Technology State Key Lab on Intelligent Technology and Systems National Lab for
Automated Lecture Transcription
Automated Lecture Transcription Brandon Muramatsu mura@mit.edu MIT, Office of Educational Innovation and Technology (Really, I m just moonlighting as an OCWC Staffer ) Citation: Muramatsu, B. (2009). Automated
Military Community and Family Policy Social Media. Guide. Staying Connected
Military Community and Family Policy Social Media Guide Staying Connected Introduction...3 Table of Contents Social Media Tools and Platforms...3 Social Networks...3 Blogs and Microblogs...7 Podcasts...8
Pavlo Baron. Big Data and CDN
Pavlo Baron Big Data and CDN Pavlo Baron www.pbit.org pb@pbit.org @pavlobaron What is Big Data Big Data describes datasets that grow so large that they become awkward to work with using on-hand database
PROMT Technologies for Translation and Big Data
PROMT Technologies for Translation and Big Data Overview and Use Cases Julia Epiphantseva PROMT About PROMT EXPIRIENCED Founded in 1991. One of the world leading machine translation provider DIVERSIFIED
Recent developments in machine translation policy at the European Patent Office
Recent developments in machine translation policy at the European Patent Office Dr Georg Artelsmair Director European Co-operation European Patent Office Brussels, 17 November 2010 The European Patent
Tanja Wissik. COTSOES Terminology and Documentation Working Group Meeting, 13 May 2013, Stockholm
Tanja Wissik COTSOES Terminology and Documentation Working Group Meeting, 13 May 2013, Stockholm What is LISE? Main pillars of the LISE Project LISE Service Version and IATE use case Workflow Research
AP GERMAN LANGUAGE AND CULTURE EXAM 2015 SCORING GUIDELINES
AP GERMAN LANGUAGE AND CULTURE EXAM 2015 SCORING GUIDELINES Identical to Scoring Guidelines used for French, Italian, and Spanish Language and Culture Exams Interpersonal Writing: E-mail Reply 5: STRONG
ICT Project on Text Transcription of Technical Video Lectures and Creation of Video Searchable Index, Metadata and Online Quizzes
ICT Project on Text Transcription of Technical Video Lectures and Creation of Video Searchable Index, Metadata and Online Quizzes Status Report up to September 30, 2010 Project duration: April 2009 to
IsumaTV. Media Player Setup Manual COOP Cable System. Media Player
IsumaTV Media Player Setup Manual COOP Cable System Visual Setup Front: Router Scan Converter Media Player Video Modulator Equipment: Media Player: Router: Scan Converter: Video Modulator: This computer
Thomas Rümmler AIT GmbH & Co. KG Christian Schlag AIT GmbH & Co. KG. Central Build and Release Management with TFS
Thomas Rümmler AIT GmbH & Co. KG Christian Schlag AIT GmbH & Co. KG Central Build and Release Management with TFS 2 OUR DAILY MOTIVATION It s hard enough for software developers to write code that works
FUNDAMENTAL TECHNOLOGIES FOR IP TRANSLATION SERVICES
FUNDAMENTAL TECHNOLOGIES FOR IP TRANSLATION SERVICES 2 The strength of intellectual property documents, including international patent applications, relies in part on the quality of the translation. While
Machine Translation and the Translator
Machine Translation and the Translator Philipp Koehn 8 April 2015 About me 1 Professor at Johns Hopkins University (US), University of Edinburgh (Scotland) Author of textbook on statistical machine translation
Automatic Mining of Internet Translation Reference Knowledge Based on Multiple Search Engines
, 22-24 October, 2014, San Francisco, USA Automatic Mining of Internet Translation Reference Knowledge Based on Multiple Search Engines Baosheng Yin, Wei Wang, Ruixue Lu, Yang Yang Abstract With the increasing
A text document of what was said and any other relevant sound in the media (often posted with audio recordings).
Video Captioning ADA compliant captions: Are one to three (preferably two) lines of text that appear on-screen all at once o Have no more than 32 characters to a line Appear on screen for 2 to 6 seconds
How to become a successful language learner
How to become a successful language learner By Alison Fenner English and German Co-ordinator, Institution Wide Language Programme Introduction Your success in learning a language depends on you! You may
Automated Multilingual Text Analysis in the Europe Media Monitor (EMM) Ralf Steinberger. European Commission Joint Research Centre (JRC)
Automated Multilingual Text Analysis in the Europe Media Monitor (EMM) Ralf Steinberger European Commission Joint Research Centre (JRC) https://ec.europa.eu/jrc/en/research-topic/internet-surveillance-systems
Product Quality and Environmental Standards: The Effect of an International Environmental Agreement on Tropical Timber Trade
Please scroll down for the English version Sehr geehrte Abonnentinnen und Abonnenten, wir freuen uns, Sie per Newsletter über die neuesten Entwicklungen des FIW-Projekts informieren zu dürfen. Dieses Mal
Clarity High School Student Survey
Clarity High School Student Survey Instructions Take 10 minutes to help your school with technology in the classroom. This is an anonymous survey regarding your technology use. It will take approximately
Website Design Application Form
Website Design Application Form The following three part questionnaire will help us estimate the timeline and cost of your new website. To give you the best quality web design directly depends on us clearly
Collaborative Machine Translation Service for Scientific texts
Collaborative Machine Translation Service for Scientific texts Patrik Lambert patrik.lambert@lium.univ-lemans.fr Jean Senellart Systran SA senellart@systran.fr Laurent Romary Humboldt Universität Berlin
A TYPICAL DAY WITH OFFICE 365. Office 365 is so much more than just an online version of email and Office 2013.
A TYPICAL DAY WITH OFFICE 365 Office 365 is so much more than just an online version of email and Office 2013. While all the documentation and information focuses on the key features and benefits of Office
32 Benefits of Pipeliner CRM
SLIDE DECK: 32 Benefits of r CRM www.pipelinersales.com 32 Benefits of r CRM r originally was designed as a tool for sales empowerment. With its newest release r meets the highest requirements for enterprise
SYSTRAN Enterprise Server 7 Online Tools User Guide. Chapter 1: Overview... 1 SYSTRAN Enterprise Server 7 Overview... 2
g SYSTRAN Enterprise Server 7 Online Tools User Guide i Table of Contents Chapter 1: Overview... 1 SYSTRAN Enterprise Server 7 Overview... 2 SYSTRAN Enterprise Server 7 Components... 2 SYSTRAN Translation
KantanMT.com. www.kantanmt.com. The world s #1 MT Platform. No Hardware. No Software. No Hassle MT.
KantanMT.com No Hardware. No Software. No Hassle MT. The world s #1 MT Platform Communicate globally, easily! Create customized language solutions in the cloud. www.kantanmt.com What is KantanMT.com? KantanMT
MY MOTHER TEOFILA REICH-RANICKI Andrew Ranicki Edinburgh German Circle, 28th February, 2012
1 MY MOTHER TEOFILA REICH-RANICKI Andrew Ranicki Edinburgh German Circle, 28th February, 2012 2 Timeline 12th March, 1920. Born in Lódź, Poland. 22nd July, 1942. Married to Marcel in the Warsaw Ghetto.
How To Manage Build And Release With Tfs 2013
#dwx14 feedback@developer-week.de #dwx14 Central Build and Release Management with TFS Thomas Rümmler AIT GmbH & Co. KG Christian Schlag AIT GmbH & Co. KG 1 2 OUR DAILY MOTIVATION It s hard enough for
Maskinöversättning 2008. F2 Översättningssvårigheter + Översättningsstrategier
Maskinöversättning 2008 F2 Översättningssvårigheter + Översättningsstrategier Flertydighet i källspråket poäng point, points, credit, credits, var verb ->was, were pron -> each adv -> where adj -> every
METADATA GENERATION FOR CULTURAL HERITAGE
METADATA GENERATION FOR CULTURAL HERITAGE Creative Histories The Josefsplatz Experience Brigitte Krenn, Gregor Sieber, Hans Petschar {brigitte.krenn, gregor.sieber}@ofai.at hans.petschar@onb.ac.at Talk
Statistical Pattern-Based Machine Translation with Statistical French-English Machine Translation
Statistical Pattern-Based Machine Translation with Statistical French-English Machine Translation Jin'ichi Murakami, Takuya Nishimura, Masato Tokuhisa Tottori University, Japan Problems of Phrase-Based
MOVING MACHINE TRANSLATION SYSTEM TO WEB
MOVING MACHINE TRANSLATION SYSTEM TO WEB Abstract GURPREET SINGH JOSAN Dept of IT, RBIEBT, Mohali. Punjab ZIP140104,India josangurpreet@rediffmail.com The paper presents an overview of an online system
Mit einem Auge auf den mathema/schen Horizont: Was der Lehrer braucht für die Zukun= seiner Schüler
Mit einem Auge auf den mathema/schen Horizont: Was der Lehrer braucht für die Zukun= seiner Schüler Deborah Löwenberg Ball und Hyman Bass University of Michigan U.S.A. 43. Jahrestagung für DidakEk der
Year 7. Home Learning Booklet. French and German Skills
Year 7 Home Learning Booklet French and German Skills Name: Tutor Group: Teacher: Given out: 1 st July Hand in: 8 th July Parent/Carer Comment: Staff Comment: Target: This homework booklet practises some
> How it works FAQ - TV SYNCED ADS. 1. TV-Synced Ads : Ok, but what is it exactly? 2. Why is TV-Synced ads relevant?
FAQ - TV SYNCED ADS > How it works The reason for syncing ads from TV to digital is simple. More and more people turn their attention away from the TV screen during the ad break. They login to check their