A Comparison of Free Online Machine Language Translators Mahesh Vanjani Jesse H
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Context in Neural Machine Translation: a Review of Models and Evaluations
Context in Neural Machine Translation: A Review of Models and Evaluations Andrei Popescu-Belis University of Applied Sciences of Western Switzerland (HES–SO) School of Management and Engineering Vaud (HEIG–VD) Route de Cheseaux 1, CP 521 1401 Yverdon-les-Bains, Switzerland [email protected] January 29, 2019 Abstract features when translating entire texts, especially when this is vital for correct translation. Taking textual con- This review paper discusses how context has been text into account1 means modeling long-range depen- used in neural machine translation (NMT) in the dencies between words, phrases, or sentences, which past two years (2017–2018). Starting with a brief are typically studied by linguistics under the topics of retrospect on the rapid evolution of NMT models, discourse and pragmatics. When it comes to transla- the paper then reviews studies that evaluate NMT tion, the capacity to model context may improve certain output from various perspectives, with emphasis on those analyzing limitations of the translation of translation decisions, e.g. by favoring a better lexical contextual phenomena. In a subsequent version, choice thanks to document-level topical information, the paper will then present the main methods that or by constraining pronoun choice thanks to knowledge were proposed to leverage context for improving about antecedents. translation quality, and distinguishes methods that This review paper puts into perspective the signif- aim to improve the translation of specific phenom- icant amount of studies devoted in 2017 and 2018 to ena from those that consider a wider unstructured improve the use of context in NMT and measure these context. -
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 .................................................................................................................................................. -
Translation and the Internet: Evaluating the Quality of Free Online Machine Translators
Quaderns. Rev. trad. 17, 2010 197-209 Translation and the Internet: Evaluating the Quality of Free Online Machine Translators Stephen Hampshire Universitat Autònoma de Barcelona Facultat de Traducció i d’Interpretació 08193 Bellaterra (Barcelona). Spain [email protected] Carmen Porta Salvia Universitat de Barcelona Facultat de Belles Arts [email protected] Abstract The late 1990s saw the advent of free online machine translators such as Babelfish, Google Translate and Transtext. Professional opinion regarding the quality of the translations provided by them, oscillates wildly from the «laughably bad» (Ali, 2007) to «a tremendous success» (Yang and Lange, 1998). While the literature on commercial machine translators is vast, there are only a handful of studies, mostly in blog format, that evaluate and rank free online machine translators. This paper offers a review of the most significant contributions in that field with an emphasis on two key issues: (i) the need for a ranking system; (ii) the results of a ranking system devised by the authors of this paper. Our small-scale evaluation of the performance of ten free machine trans- lators (FMTs) in «league table» format shows what a user can expect from an individual FMT in terms of translation quality. Our rankings are a first tentative step towards allowing the user to make an informed choice as to the most appropriate FMT for his/her source text and thus pro- duce higher FMT target text quality. Key words: free online machine translators, evaluation, internet, ranking system. Resum Durant la darrera dècada del segle xx s’introdueixen els traductors online gratuïts (TOG), com poden ser Babelfish, Google Translate o Transtext. -
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. -
A Comparison of Free Online Machine Language Translators Mahesh Vanjani1,* and Milam Aiken2 1Jesse H
Journal of Management Science and Business Intelligence, 2020, 5–1 July. 2020, pages 26-31 doi: 10.5281/zenodo.3961085 http://www.ibii-us.org/Journals/JMSBI/ ISBN 2472-9264 (Online), 2472-9256 (Print) A Comparison of Free Online Machine Language Translators Mahesh Vanjani1,* and Milam Aiken2 1Jesse H. Jones School of Business, Texas Southern University, 3100 Cleburne Street, Houston, TX 77004 2School of Business Administration, University of Mississippi, University, MS 38677 *Email: [email protected] Received on 5/15/2020; revised on 7/25/2020; published on 7/26/2020 Abstract Automatic Language Translators also referred to as machine translation software automate the process of language translation without the intervention of humans While several automated language translators are available online at no cost there are large variations in their capabilities. This article reviews prior tests of some of these systems, and, provides a new and current comprehensive evaluation of the following eight: Google Translate, Bing Translator, Systran, PROMT, Babylon, WorldLingo, Yandex, and Reverso. This re- search could be helpful for users attempting to explore and decide which automated language translator best suits their needs. Keywords: Automated Language Translator, Multilingual, Communication, Machine Translation published results of prior tests and experiments we present a new and cur- 1 Introduction rent comprehensive evaluation of the following eight electronic automatic language translators: Google Translate, Bing Translator, Systran, Automatic Language Translators also referred to as machine translation PROMT, Babylon, WorldLingo, Yandex, and Reverso. This research software automate the process of language translation without the inter- could be helpful for users attempting to explore and decide which auto- vention of humans. -
Machine Translation in the Field of Law: a Study of the Translation of Italian Legal Texts Into German
Comparative Legilinguistics vol. 37/2019 DOI: http://dx.doi.org/10.14746/cl.2019.37.4 MACHINE TRANSLATION IN THE FIELD OF LAW: A STUDY OF THE TRANSLATION OF ITALIAN LEGAL TEXTS INTO GERMAN EVA WIESMANN, Prof., PhD Department of Interpreting and Translation, University of Bologna Corso della Repubblica 136, 47121 Forlì, Italy [email protected] ORCID: https://orcid.org/0000-0001-9414-8038 Abstract: With the advent of the neural paradigm, machine translation has made another leap in quality. As a result, its use by trainee translators has increased considerably, which cannot be disregarded in translation pedagogy. However, since legal texts have features that pose major challenges to machine translation, the question arises as to what extent machine translation is now capable of translating legal texts or at least certain types of legal text into another legal language well enough so that the post- editing effort is limited, and, consequently, whether a targeted use in translation pedagogy can be considered. In order to answer this question, DeepL Translator, a machine translation system, and MateCat, a CAT system that integrates machine translation, were tested. The test, undertaken at different times and without specific translation memories, provided Eva Wiesmann: Machine Translation in… for the translation of several legal texts of different types utilising both systems, and was followed by systematisation of errors and evaluation of translation results. The evaluation was carried out according to the following criteria: 1) comprehensibility and meaningfulness of the target text; and 2) correspondence between source and target text in consideration of the specific translation situation. -
Automated Translation in Typo3
AUTOMATED TRANSLATION IN TYPO3 Whitepaper on Translating contents automatically using DeepL and Google Translate in TYPO3 backend by Georgy GE What is TYPO3? TYPO3 CMS is an Open Source Enterprise Content Management System with a large global community. TYPO3 is written in PHP scripting language and was initially authored by Dane Kasper Skårhøj in 1997. The standout features of TYPO3 is its diversity and modularity. It can keep running on multiple web servers like Apache, Nginx or IIS and over numerous operating systems such as Linux, Microsoft Windows, FreeBSD, Mac OS X and OS/2. TYPO3 provides the basis for modern content management that can be adapted by small business websites to large multi-lingual global enterprises portals. TYPO3 always keeps a check on the updated requirements of businesses and public institutions. TYPO3 is opted by small and medium enterprises and municipalities because of its license- cost-free open source approach. In addition to the basic set of interfaces, functions and modules, TYPO3's functionality spectrum can be implemented using extensions. TYPO3 allows users or website operators to upgrade the corresponding websites using a solid extensions framework, and helps to publish and deliver any form of content to multiple devices. During the course of development of a TYPO3 website, a developer can rely on a strong community and about 6,000 extensions that offer unlimited possibilities. Ph : +41 43 558 4360 E-mail: [email protected] Visit: www.pitsolutions.com 2 Why TYPO3? 1. No license cost TYPO3 is an open source software under the GNU General Public License with no license fee. -
The IALLT Journal a Publication of the International Association for Language Learning Technology
The IALLT Journal A publication of the International Association for Language Learning Technology Measuring the Impact of Online Translation on FL Writing Scores Errol M. O’Neill University of Memphis ABSTRACT Online translation (OT) sites, which automatically convert text from one language to another, have been around for nearly 20 years. While foreign language students and teachers have long been aware of their existence, and debates about the accuracy and usefulness of OT are well known, surprisingly little research has been done to analyze the actual effects of online translator usage on student writing. The current study compares the scores of two composition tasks by third- and fourth-semester university students of French who used an online translator, with or without prior training, to the scores of students who did not use OT. Students using an online translator did not perform significantly worse those not using the translator on either task. In fact, students who received prior training in OT outscored the control group overall on the second writing task. Additionally, students using the online translator received higher subscores on one or both writing tasks for features such as comprehensibility, spelling, content, and grammar. The results of the current study are discussed in detail; implications for the foreign language classroom are presented; and avenues for future research are proposed. Vol. 46 (2) 2016 Measuring the Impact of Online Translation on FL Writing Scores INTRODUCTION As the world becomes increasingly connected through the use of the Internet and related technologies, foreign language (FL) students also have access to the same resources available to the general public. -
An Evaluation of the Accuracy of Online Translation Systems
Communications of the IIMA Volume 9 Issue 4 Article 6 2009 An Evaluation of the Accuracy of Online Translation Systems Milam Aiken University of Mississippi Kaushik Ghosh University of Mississippi John Wee University of Mississippi Mahesh Vanjani University of Mississippi Follow this and additional works at: https://scholarworks.lib.csusb.edu/ciima Recommended Citation Aiken, Milam; Ghosh, Kaushik; Wee, John; and Vanjani, Mahesh (2009) "An Evaluation of the Accuracy of Online Translation Systems," Communications of the IIMA: Vol. 9 : Iss. 4 , Article 6. Available at: https://scholarworks.lib.csusb.edu/ciima/vol9/iss4/6 This Article is brought to you for free and open access by CSUSB ScholarWorks. It has been accepted for inclusion in Communications of the IIMA by an authorized editor of CSUSB ScholarWorks. For more information, please contact [email protected]. Examination of the Accuracy of Online Translation Systems Aiken, Ghosh, Wee & Vanjani An Evaluation of the Accuracy of Online Translation Systems Milam Aiken University of Mississippi USA [email protected] Kaushik Ghosh University of Mississippi USA [email protected] John Wee University of Mississippi USA [email protected] Mahesh Vanjani Texas Southern University USA [email protected] Abstract Until fairly recently, translation among a large variety of natural languages has been difficult and costly. Now, several free, Web-based machine translation (MT) services can provide support, but relatively little research has been conducted on their accuracies. A study of four of these services using German-to-English and Spanish-to-English translations showed that Google Translate appeared to be superior. Further study using this system alone showed that while translations were not always perfect, their understandability was quite high. -
Report About a Survey-Based Research on Machine Translation Post-Editing
REPORT ABOUT A SURVEY‐BASED RESEARCH ON MACHINE TRANSLATION POST‐EDITING Common ground and gaps between LSCs, linguists and trainers Clara Ginovart Cid Universitat Pompeu Fabra Barcelona 22/12/2020 This report presents the findings of a survey‐based research based on three online questionnaires. It is part of the Industrial Doctorates research n° “2017 DI 010”. One questionnaire is addressed to Language Service Companies (LSCs) that sell or use as a process machine translation post‐editing (MTPE, also abbreviated PEMT). It received 66 submissions. Another questionnaire is addressed to individual linguists (inhouse or freelance) who accept MTPE assignments, and it received 141 submissions. Finally, the third questionnaire is addressed to European master or postgraduate PE educators, and it received 54 submissions. The survey‐based research is completed in between the end of 2018 and the beginning of 2019. In this report, the content of each questionnaire is presented in the first section (1. Content of the Questionnaires). In the second section (2. Results of the Submissions), the data of their findings is displayed, where the percentages have been rounded off. The three related publications where the findings are discussed are cited below: Ginovart Cid C, Colominas C, Oliver A. Language industry views on the profile of the post‐ editor. Translation Spaces. 2020 Jan 14. DOI:10.1075/ts.19010.cid Ginovart Cid C. The Professional Profile of a Post‐editor according to LSCs and Linguists: a Survey‐Based Research. HERMES ‐ Journal of Language and Communication in Business, 60, 171‐190. 2020 Jul 08. DOI:10.7146/hjlcb.v60i0.121318 Ginovart Cid C, Oliver A. -
Assessing the Comprehensibility and Perception of Machine Translations
ASSESSING THE COMPREHENSIBILITY AND PERCEPTION OF MACHINE TRANSLATIONS A PILOT STUDY Aantal woorden: 16 804 Iris Ghyselen Studentennummer: 01400320 Promotor: Prof. dr. Lieve Macken Masterproef voorgelegd voor het behalen van de graad master in het vertalen in de richting Toegepaste Taalkunde Academiejaar: 2017 – 2018 ASSESSING THE COMPREHENSIBILITY AND PERCEPTION OF MACHINE TRANSLATIONS A PILOT STUDY Aantal woorden: 16 804 Iris Ghyselen Studentennummer: 01400320 Promotor: Prof. dr. Lieve Macken Masterproef voorgelegd voor het behalen van de graad master in het vertalen in de richting Toegepaste Taalkunde Academiejaar: 2017 – 2018 i VERKLARING I.V.M. AUTEURSRECHT De auteur en de promotor(en) geven de toelating deze studie als geheel voor consultatie beschikbaar te stellen voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen van het auteursrecht, in het bijzonder met betrekking tot de verplichting de bron uitdrukkelijk te vermelden bij het aanhalen van gegevens uit deze studie. ii ACKNOWLEDGMENTS There are several people who deserve my profound gratitude for their help and support while I was writing this dissertation. In the first place I would like to give thanks to all the respondents who filled in my questionnaire. That small act of kindness meant a great deal to me. The questionnaire required some time and attention to fill in and was distributed in a period when people were being overwhelmed on social media with all kinds of questionnaires for other theses. Therefore, I am very grateful to each and every one of them who helped me. Secondly, I would like to express my deep sense of gratitude to my supervisor, Prof. -
Google Translate: Everyone’S Language Wallah
STEPHEN E ARNOLD September 1, 2009 Google Translate: Everyone’s Language Wallah Introduction Old joke: If you can speak three languages, you are trilingual. If you speak two languages, you are bilingual. If you speak one language, you are an American. The problem is that other languages exist. In Brazil, educated professionals speak Portuguese and two or three other languages. For everyday work and communication, Portuguese is still the dominant language. The same fact holds true in most economic powerhouses. In order to keep pace with information available on Web sites, in Web logs, and even Tweets, translating a source language into my native language (English) is becoming more important. Even though I lived in Brazil and had a working knowledge of Portuguese, I need software safety nets. Like many knowledge workers, I have relied upon software that takes a source language such as French, German, or Japanese and translates it into English. I have a number of translation resources. I use some open source tools built on the GNU gettext framework (http://www.gnu.org/software/gettext/); for example, code from Google’s gettext commons (http://code.google.com/p/gettext-commons/). I have experimented with a range of shareware products. If you want to give some of these systems a trial run, navigate to ITShareware.com (http://www.itshareware.com/catlist-code_58-start_0-sort_0.htm). When AltaVista.com was the big dog in search, I found the Babel Fish online translation service useful. Now part of the Yahoo suite of online services, Babel Fish (based on Systran’s technology) can handle some light-weight translation tasks.