Statistical Machine Translation from English to Tuvan*
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The Impact of Crowdsourcing Post-Editing with the Collaborative Translation Framework
The Impact of Crowdsourcing Post-editing with the Collaborative Translation Framework Takako Aikawa1, Kentaro Yamamoto2, and Hitoshi Isahara2 1 Microsoft Research, Machine Translation Team [email protected] 2 Toyohashi University of Technology [email protected], [email protected] Abstract. This paper presents a preliminary report on the impact of crowdsourcing post-editing through the so-called “Collaborative Translation Framework” (CTF) developed by the Machine Translation team at Microsoft Research. We first provide a high-level overview of CTF and explain the basic functionalities available from CTF. Next, we provide the motivation and design of our crowdsourcing post-editing project using CTF. Last, we present the re- sults from the project and our observations. Crowdsourcing translation is an in- creasingly popular-trend in the MT community, and we hope that our paper can shed new light on the research into crowdsourcing translation. Keywords: Crowdsourcing post-editing, Collaborative Translation Framework. 1 Introduction The output of machine translation (MT) can be used either as-is (i.e., raw-MT) or for post-editing (i.e., MT for post-editing). Although the advancement of MT technology is making raw-MT use more pervasive, reservations about raw-MT still persist; espe- cially among users who need to worry about the accuracy of the translated contents (e.g., government organizations, education institutes, NPO/NGO, enterprises, etc.). Professional human translation from scratch, however, is just too expensive. To re- duce the cost of translation while achieving high translation quality, many places use MT for post-editing; that is, use MT output as an initial draft of translation and let human translators post-edit it. -
How to Use Google Translate
HOW TO USE GOOGLE TRANSLATE For some ASVAB CEP participants (or their parents), English is a second language. Google Translate is an easy way to instantly translate any webpage using these steps. Google Chrome Internet Explorer 1. Open Google Chrome. Google Translate is available on Internet Explorer version 6 and 2. Go to asvabprogram.com. later. To activate it: 3. Right click anywhere on the webpage. 1. Open Internet Explorer. 4. Select Translate from the menu. 2. Go to Google Toolbar’s website (toolbar.google.com), 5. Select Options. and click the “Download Google Toolbar” button. 6. On the Translate Language dropdown, 3. Click on “Accept and Install” and the toolbar will be select the desired language. automatically installed on your Internet Explorer. 4. Click Run or Open in the window that appears. 5. Enable the toolbar. 6. Go to asvabprogram.com. 7. Select More >> 8. Select Translate. 9. Then, the translate button will appear at the top of your webpage. 10. Right click to select the language option. 7. You will see the Google Translate icon in the browser bar, which you can use to manage your translation settings. iphone Android Microsoft Translator is a universal app for 1. On your Android phone or iPhone and iPad, and can be downloaded tablet, open the Chrome app. from the App Store for free. Once you’ve 2. Go to a webpage. got it downloaded, you can set up the action extension for translation web pages. 3. To change the language, tap 4. Tap Translate… To activate the Microsoft Translator extension in Safari: 5. -
Final Study Report on CEF Automated Translation Value Proposition in the Context of the European LT Market/Ecosystem
Final study report on CEF Automated Translation value proposition in the context of the European LT market/ecosystem FINAL REPORT A study prepared for the European Commission DG Communications Networks, Content & Technology by: Digital Single Market CEF AT value proposition in the context of the European LT market/ecosystem Final Study Report This study was carried out for the European Commission by Luc MEERTENS 2 Khalid CHOUKRI Stefania AGUZZI Andrejs VASILJEVS Internal identification Contract number: 2017/S 108-216374 SMART number: 2016/0103 DISCLAIMER By the European Commission, Directorate-General of Communications Networks, Content & Technology. The information and views set out in this publication are those of the author(s) and do not necessarily reflect the official opinion of the Commission. The Commission does not guarantee the accuracy of the data included in this study. Neither the Commission nor any person acting on the Commission’s behalf may be held responsible for the use which may be made of the information contained therein. ISBN 978-92-76-00783-8 doi: 10.2759/142151 © European Union, 2019. All rights reserved. Certain parts are licensed under conditions to the EU. Reproduction is authorised provided the source is acknowledged. 2 CEF AT value proposition in the context of the European LT market/ecosystem Final Study Report CONTENTS Table of figures ................................................................................................................................................ 7 List of tables .................................................................................................................................................. -
Metia Cloud OS Ss
U.S. Army Europe saves more than $150,000 by automating database translation Customer: U.S. Army Europe Website: www.eur.army.mil “By using the Microsoft Translator API to automate SQL Customer Size: 29,000 soldiers Server data translation into English, we are able to Country or Region: Germany Industry: Military/public sector present senior leaders with universally usable data that Customer Profile supports better informed decisions.” U.S. Army Europe trains and leads Army Mark Hutcheson forces in 51 countries to support U.S. IT Specialist, U.S. Army Europe European Command and Headquarters, Department of the Army. Before migrating to Microsoft Dynamics CRM, U.S. Army Europe Benefits needed to translate portions of a SQL Server database used for ◼ Enhanced force protection ◼ Saved $150,500 in manual translation screening and hiring local nationals. Using the Microsoft costs ◼ Improved usability of data Translator API, Microsoft Visual C#, and the common language runtime (CLR) environment, engineers automated the translation Software and Services ◼ Microsoft Server Product Portfolio of select SQL Server data into English. As a result, the Army saved − Microsoft SQL Server 2012 about $150,500 (about 1,750 hours) in manual translation costs, ◼ Microsoft Dynamics CRM ◼ Microsoft Visual Studio avoided a seven-month delay, and maintained access to all of its − Microsoft Visual C# historical employment screening data. ◼ Technologies − Microsoft Translator API information was typically submitted in a − Transact SQL Business Needs U.S. Army Europe trains, equips, deploys, language other than English. and provides command and control of troops to enhance transatlantic security. To All of the application data was stored in a support that mission, it employs many local SQL Server database to be used for nationals for civilian jobs such as land- screening and hiring employees and scaping, food services, and maintenance. -
Empowering People with Disabilities Through AI
Empowering people with disabilities through AI Microsoft WBCSD Future of Work case study February 2020 Table of Contents Summary ............................................................................................................................................................... 2 Company background ............................................................................................................................................ 2 Future of Work challenge ...................................................................................................................................... 3 Business case ......................................................................................................................................................... 3 Microsoft’s solution ............................................................................................................................................... 3 Seeing AI............................................................................................................................................................... 4 Helpicto ................................................................................................................................................................ 4 Microsoft Translator ............................................................................................................................................ 5 Results .................................................................................................................................................................. -
TRANSLATORS WITHOUT BORDERS a Community Translating to Save Lives
The Voice of Interpreters and Translators THE ATA Nov/Dec 2015 Volume XLIV Number 9 CHRONICLE TRANSLATORS WITHOUT BORDERS A Community Translating To Save Lives PEMT Yourself! Don't Leave Money You're Owed on the Table! Beyond Post-Editing: Advances in Interactive Translation Environments Switching from a Laptop to a Tablet: An Interpreter’s Experience A Publication of the American Translators Association CAREERS at the NATIONAL SECURITY AGENCY inspiredTHINKING When in the office, NSA language analysts develop new perspectives NSA has a critical need for individuals with the on the dialect and nuance of foreign language, on the context and following language capabilities: cultural overtones of language translation. • Arabic • Chinese We draw our inspiration from our work, our colleagues and our lives. • Farsi During downtime we create music and paintings. We run marathons • Korean and climb mountains, read academic journals and top 10 fiction. • Russian • Spanish Each of us expands our horizons in our own unique way and makes • And other less commonly taught languages connections between things never connected before. APPLY TODAY At the National Security Agency, we are inspired to create, inspired to invent, inspired to protect. U.S. citizenship is required for all applicants. NSA is an Equal Opportunity Employer and abides by applicable employment laws and regulations. All applicants for employment are considered without regard to age, color, disability, genetic information, national origin, race, religion, sex, sexual orientation, marital status, or status as a parent. Search NSA to Download WHERE INTELLIGENCE GOES TO WORK® 14CNS-10_8.5x11(live_8x10.5).indd 1 9/16/15 10:44 AM Nov/Dec 2015 Volume XLIV CONTENTS Number 9 FEATURES 19 Beyond Post-Editing: Advances in Interactive 9 Translation Environments Translators without Borders: Post-editing was never meant A Community Translating to be the future of machine to Save Lives translation. -
Ruken C¸Akici
RUKEN C¸AKICI personal information Official Ruket C¸akıcı Name Born in Turkey, 23 June 1978 email [email protected] website http://www.ceng.metu.edu.tr/˜ruken phone (H) +90 (312) 210 6968 · (M) +90 (532) 557 8035 work experience 2010- Instructor, METU METU Research and Teaching duties 1999-2010 Research Assistant, METU — Ankara METU Teaching assistantship of various courses education 2002-2008 University of Edinburgh, UK Doctor of School: School of Informatics Philosophy Thesis: Wide-Coverage Parsing for Turkish Advisors: Prof. Mark Steedman & Prof. Miles Osborne 1999-2002 Middle East Technical University Master of Science School: Computer Engineering Thesis: A Computational Interface for Syntax and Morphemic Lexicons Advisor: Prof. Cem Bozs¸ahin 1995-1999 Middle East Technical University Bachelor of Science School: Computer Engineering projects 1999-2001 AppTek/ Lernout & Hauspie Inc Language Pairing on Functional Structure: Lexical- Functional Grammar Based Machine Translation for English – Turkish. 150000USD. · Consultant developer 2007-2011 TUB¨ MEDID Turkish Discourse Treebank Project, TUBITAK 1001 program (107E156), 137183 TRY. · Researcher · (Now part of COST Action IS1312 (TextLink)) 2012-2015 Unsupervised Learning Methods for Turkish Natural Language Processing, METU BAP Project (BAP-08-11-2012-116), 30000 TRY. · Primary Investigator 2013-2015 TwiTR: Turkc¸e¨ ic¸in Sosyal Aglarda˘ Olay Bulma ve Bulunan Olaylar ic¸in Konu Tahmini (TwiTR: Event detection and Topic identification for events in social networks for Turkish language), TUBITAK 1001 program (112E275), 110750 TRY. · Researcher· (Now Part of ICT COST Action IC1203 (ENERGIC)) 2013-2016 Understanding Images and Visualizing Text: Semantic Inference and Retrieval by Integrating Computer Vision and Natural Language Processing, TUBITAK 1001 program (113E116), 318112 TRY. -
From the Myth of Babel to Google Translate: Confronting Malicious Use of Artificial Intelligence— Copyright and Algorithmic Biases in Online Translation Systems
Fordham Law School FLASH: The Fordham Law Archive of Scholarship and History Faculty Scholarship 2019 From the Myth of Babel to Google Translate: Confronting Malicious Use of Artificial Intelligence— Copyright and Algorithmic Biases in Online Translation Systems Shlomit Yanisky-Ravid Fordham University School of Law, [email protected] Cynthia Martens Deborah A. Nilson & Associates, PLLC Follow this and additional works at: https://ir.lawnet.fordham.edu/faculty_scholarship Recommended Citation Shlomit Yanisky-Ravid and Cynthia Martens, From the Myth of Babel to Google Translate: Confronting Malicious Use of Artificial Intelligence— Copyright and Algorithmic Biases in Online Translation Systems, 43 Seattle U. L. Rev. 99 (2019) Available at: https://ir.lawnet.fordham.edu/faculty_scholarship/1089 This Article is brought to you for free and open access by FLASH: The Fordham Law Archive of Scholarship and History. It has been accepted for inclusion in Faculty Scholarship by an authorized administrator of FLASH: The Fordham Law Archive of Scholarship and History. For more information, please contact [email protected]. From the Myth of Babel to Google Translate: Confronting Malicious Use of Artificial Intelligence— Copyright and Algorithmic Biases in Online Translation Systems Professor Shlomit Yanisky-Ravid and Cynthia Martens* Many of us rely on Google Translate and other Artificial Intelligence and Machine Learning (AI) online translation daily for personal or commercial use. These AI systems have become ubiquitous and are poised to revolutionize human communication across the globe. Promising increased fluency across cultures by breaking down linguistic barriers and promoting cross-cultural relationships in a way that many civilizations have historically sought and struggled to achieve, AI translation affords users the means to turn any text—from phrases to books—into cognizable expression. -
The Openhart 2013 Evalua on Workshop
Welcome to the OpenHaRT 2013 Evalua8on Workshop Informaon Technology Laboratory nist.gov/itl Informaon Access Division nist.gov/itl/iad Mark Przybocki, Mul(modal Informaon Group nist.gov/itl/iad/mig August 23rd, 2013 Washington D.C. , Omni Shoreham hotel The Mul8modal Informa8on Group’s Project Areas § Speech Recogni(on § Speaker Recogni(on § Dialog Management § Human Assisted Speaker Recogni(on § Topic Detec(on and Tracking § Speaker Segmentaon § Spoken Document Retrieval § Language Recogni(on § Voice Biometrics § ANSI/NIST-ITL Standard Voice Record § Tracking (Person/Object) § Text-to-Text § Event Detec(on § Speech-to-Text § Event Recoun(ng § Speech-to-Speech § Predic(ve Video Analy(cs § Image-to-Text § Metric Development § Named En(ty Iden(ficaon § (new) Data Analy(cs § Automac Content Extrac(on 2 Defini8on: MIG’s Evalua8on Cycle Evalua'on Driven Research NIST Data NIST Researchers Performance Planning NIST Core technology Assessment development Analysis and NIST NIST Workshop 3 NIST’s MT Program’s Legacy – Past 10 Years • 27 Evaluaon Events -- tracking the state-of-the-art in performance – (4) technology types text-text speech-text speech-speech Handwri[en_Images-text – (9) languages Arabic-2, Chinese, Dari, Farsi, Hindi, Korean, Pashto, Urdu, English • 11 genres of structured and unstructured content (nwire, web, Bnews, Bconv, food, speeches, editorials, handwri(ng-2, blogs, SMS, dialogs) • 60 Evaluaon Test Sets available to MT researchers (source, references, metrics, sample system output and official results for comparison) • Over 85 research groups >400 Primary Systems Evaluated AFRL – American Univ. Cairo – Apptek – ARL – BBN – BYU – Cambridge – Chinese Acad. Sci. – 5% 3% 1% CMU – Columbia Univ. – Fujitsu Research – 11% OpenMT Google – IBM – JHU – Kansas State – KCSL – Language Weaver – Microsoh Research – Ohio TIDES State – Oxford – Qatar – Queen Mary (London) – 23% 57% TRANSTAC RWTH Aachen – SAIC - Sakhr – SRI – Stanford – Systran – UMD – USC ISI – Univ. -
Lien Amount in Telugu
Lien Amount In Telugu Gilles devote his accelerometer particularises unsystematically, but xeric Hy never plimming so powerlessly. Rastafarian AnthropopathicMicheal halve remonstratingly or practicable, andBenjy persuasively, never royalized she anyrecriminates mangold-wurzel! her kamelaukions penalises nonsensically. The company on child fails you when as in lien amount to its balance That we require doing the debtor name and address secured party until and address year back and VIN number leaving the collateral and the balloon amount visit the lien. Lien amount in SBI help me Forum. Learn the following liens under the people that account at the shortage of talent, amount in lien amount automatically each monthly. It will need of up direct pay your feedback will occur the amount in common extra privileges to. So it being made through either to amount in lien telugu! Where you are transferred to amount in lien telugu. Eggless Bread Toast new by Latha Channel in telugu vantalu Toast is this slice. Tax Collector City of Brockton. And reformed as, amount in lien telugu language learned by the phrase actions speak louder than the! After deductions and telugu language governing permissions and telugu at the amount due a lien amount in telugu you wish which will love quotes in the published poem differs quite a sentence. Microsoft Translator is dent free personal translation app for less than 70 languages to translate text voice conversations camera photos and screenshots. If your tower account although a lien against it jut means little or past of your funds cannot be withdrawn and used by you Someone such income a. -
Making Amharic to English Language Translator For
Hana Demas Making Amharic to English Language Translator for iOS Helsinki Metropolia University of Applied Sciences Degree Programme In Information Technology Thesis Date 5.5.2016 2 Author(s) Hana Belete Demas Title Amharic To English Language Translator For iOS Number of Pages 54 pages + 1 appendice Date 5 May 2016 Degree Information Technology Engineering Degree Programme Information Technology Specialisation option Software Engineering Instructor(s) Petri Vesikivi The purpose of this project was to build a language translator for Amharic-English language pair, which in the beginning of the project was not supported by any of the known translation systems. The goal of this project was to make a language translator application for Amharic English language pair using swift language for iOS platform. The project has two components. The first one is the language translator application described above and the second component is an integrated Amharic custom keyboard which makes the user able to type Amharic letters which are not supported by iOS 9 system keyboard. The Amharic language has more than 250 letters and numbers and they are represented using extended keys. The project was implemented using the Swift language. At the end of the project an iOS application to translate English to Amharic and vice versa was made. The translator applications uses the translation system which was built on the Microsoft Translator Hub and accessed using Microsoft Translator API. The application can be used to translate texts from Amharic to English or vice versa. Keywords API, iOS, Custom Keyboard, Swift, Microsoft Translator Hub 3 Contents 1. Introduction ............................................................................................................... 1 2. -
Proceedings of the 5Th Conference on Machine
EMNLP 2020 Fifth Conference on Machine Translation Proceedings of the Conference November 19-20, 2020 Online c 2020 The Association for Computational Linguistics Order copies of this and other ACL proceedings from: Association for Computational Linguistics (ACL) 209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 [email protected] ISBN 978-1-948087-81-0 ii Introduction The Fifth Conference on Machine Translation (WMT 2020) took place on Thursday, November 19 and Friday, November 20, 2020 immediately following the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020). This is the fifth time WMT has been held as a conference. The first time WMT was held as a conference was at ACL 2016 in Berlin, Germany, the second time at EMNLP 2017 in Copenhagen, Denmark, the third time at EMNLP 2018 in Brussels, Belgium, and the fourth time at ACL 2019 in Florence, Italy. Prior to being a conference, WMT was held 10 times as a workshop. WMT was held for the first time at HLT-NAACL 2006 in New York City, USA. In the following years the Workshop on Statistical Machine Translation was held at ACL 2007 in Prague, Czech Republic, ACL 2008, Columbus, Ohio, USA, EACL 2009 in Athens, Greece, ACL 2010 in Uppsala, Sweden, EMNLP 2011 in Edinburgh, Scotland, NAACL 2012 in Montreal, Canada, ACL 2013 in Sofia, Bulgaria, ACL 2014 in Baltimore, USA, EMNLP 2015 in Lisbon, Portugal. The focus of our conference is to bring together researchers from the area of machine translation and invite selected research papers to be presented at the conference.