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Module 4. How to Select a Machine Translation System
CSA Research eLearning Series: Machine Translation for Language Service Providers
by Arle Lommel, September 26, 2018, 1 Pages View abstract 
One of the primary challenges language service providers face in implementing machine translation (MT) is in selecting vendors that will meet their needs. This module delivers a process for shortlisting viable candidates and then diving into details to . . .
 
Module 5. Training Machine Translation Systems
CSA Research eLearning Series: Machine Translation for Language Service Providers
by Arle Lommel, September 26, 2018, 1 Pages View abstract 
When language service providers work with advanced MT systems, they need to train them with relevant data to improve quality and relevance to their and their clients’ needs. This module examines how implementers accomplish this vital task. It focuses . . .
 
Module 1: Machine Translation: Foundation and Fundamentals
CSA Research eLearning Series: Machine Translation for Language Service Providers
by Arle Lommel, September 21, 2018, 1 Pages View abstract 
This module in CSA’s eLearning series on machine translation (MT) provides an introduction to the basics of the technology. It defines MT technology and discusses the four basic types in use today and how they work: rule-based, statistical, hybrid, . . .
 
Module 2: Business Use Cases for Machine Translation (MT)
CSA Research eLearning Series: Machine Translation for Language Service Providers
by Arle Lommel, September 21, 2018, 1 Pages View abstract 
Machine translation (MT) can fulfill many different roles within a language service provider’s organization. This module examines four primary scenarios: MT for internal productivity, post-edited MT, providing raw MT as a service for clients, and . . .
 
Module 3: Deploying Machine Translation (MT)
CSA Research eLearning Series: Machine Translation for Language Service Providers
by Arle Lommel, September 21, 2018, 1 Pages View abstract 
Language service providers will find many different ways to obtain machine translation (MT) services today, ranging from free, online services to building their own engines. Each approach has its place to meet different demands. This module delves into . . .
 
Machine Translation: Which Type Works Best? (2018)
by Donald A. DePalma, Arle Lommel, June 28, 2018, 12 Pages View abstract 
Just a few years ago it seemed that the decade-old battle between rule-based and statistical machine translation had ended, with statistical approaches dominating and rule-based systems occupying a niche role. Hybrid technology that combines the two had . . .
 
TechStack: Machine Translation (2018)
by Arle Lommel, Donald A. DePalma, May 30, 2018, 29 Pages View abstract 
Machine translation (MT) has become an essential part of many corporate strategies for content globalization. In this brief, we provide: an overview of MT technology; a review of how it has changed over time and how it is evolving; a SWOT analysis of . . .
 
Translation Technology Adoption Patterns at LSPs
by Benjamin B. Sargent, March 21, 2018, 7 Pages View abstract 
This report explores the rates of adoption for four categories of technology used by language service providers for written-word production: translation management system (TMS), machine translation (MT), translation memory (TM), and terminology managem . . .
 
LSP Business: Percentage Adoption of Machine Translation (2017)
Six Correlations to Benchmark Your Business
by Hélène Pielmeier, March 12, 2018, 1 Pages View abstract 
This data visualization provides benchmarking data for LSPs conducting business planning. It is based on 486 survey responses conducted for CSA Research’s 13th annual report on the “The Language Services Market: 2017.”  This . . .
 
TechStack: Automated Content Enrichment
by Arle Lommel, Hélène Pielmeier, Donald A. DePalma, January 29, 2018, 15 Pages View abstract 
Automated content enrichment (ACE) is an emerging technology that parses source and target text, creates links to relevant external resources, and can supply additional information such as terms, concepts, dates, or products. ACE makes content more . . .
 
How AI Will Augment Human Translation
by Arle Lommel, October 31, 2017, 9 Pages View abstract 
Some software developers hope to leverage the latest wave of artificial intelligence research to replace human translators. Others more realistically are incorporating AI to enhance the capabilities of human translators. This report analyzes the software . . .
 
Optimizing Content for MT: A Checklist
by Arle Lommel, Rebecca Ray, May 10, 2017, 5 Pages View abstract 
Content source optimization (CSO) refers to the process of preparing text, graphics, and other content components for translation. It eliminates obstacles to efficient conversions into another language and creates material in a form suitable for multiple . . .
 
Neural MT: Sorting Fact from Fiction
by Arle Lommel, January 18, 2017, 11 Pages View abstract 
Recent developments in neural machine translation (NMT) have captured public and media attention. Predictions that this new technology will drive an artificial intelligence (AI)-based revolution in language services are common. This brief looks at: 1) . . .
 
TechStack: Machine Translation
by Arle Lommel, Donald A. DePalma, November 28, 2016, 26 Pages View abstract 
Machine translation (MT) has become an essential part of many corporate strategies for content globalization. In this brief, we provide: 1) an overview of MT technology; 2) a review of how it has changed over time and how it is evolving; 3) a SWOT analysis . . .
 
How Translators’ Perception of MT Affects Deployments
by Stephen Henderson, October 26, 2016, 11 Pages View abstract 
Freelancers sit at the front lines of translation production and their views on machine translation (MT) have a direct effect on an organization’s ability to deploy solutions. They can make or break initiatives to leverage the technology. In . . .
 
Open-Source Language-Industry Ecosystems
by Arle Lommel, September 16, 2016, 18 Pages View abstract 
Where does open-source software (OSS) fit in the language sector? This brief updates our earlier analysis of six tools – GlobalSight, Moses, Okapi, OmegaT, ]project-open[, and TermWiki – and adds information on six more solutions: LanguageTool, . . .
 
How to Select an MT System
by Arle Lommel, July 28, 2016, 13 Pages View abstract 
Increasing numbers of enterprises and language service providers (LSPs) are turning to machine translation (MT) to accelerate their global growth. Building a dedicated MT system requires a major investment of expertise, material, and cost. As a result, . . .
 
Machine Translation: Which Type Works Best?
by Donald A. DePalma, July 28, 2016, 11 Pages View abstract 
Just a few years ago it seemed that the decade-old battle between rule-based and statistical machine translation had ended, with statistical approaches dominating and rule-based systems occupying a niche role in the industry. Hybrid technology that combines . . .
 
Fast-Growing LSPs Turn to Machine Translation
by Arle Lommel, June 30, 2016, 12 Pages View abstract 
Executives at most of the LSPs we interview or survey are evaluating the role of machine translation (MT) in their businesses. They wonder where they stand compared to their peers and what competitive pressures they will face from MT and post-editing . . .
 
MT Is Unavoidable to Keep Up with Content Volumes
by Donald A. DePalma, June 17, 2016, 3 Pages View abstract 
Mainstream media report the criticisms of linguists and other specialists who decry the quality of machine translation (MT). However, language service providers experience increased pressure to adopt MT if they haven't done so yet. This brief describes . . .
 
MT Skeptics: Is the Time Right for Post-Editing?
by Arle Lommel, June 17, 2016, 7 Pages View abstract 
Post-editing is rapidly becoming a popular solution for content translation. Language service providers experience considerable pressure to adopt it for internal efficiency or to respond to client demands. However, CSA Research finds that about half of . . .
 
Post-Editing Goes Mainstream
How LSPs Use MT to Meet Client Demands
by Arle Lommel, Donald A. DePalma, June 17, 2016, 33 Pages View abstract 
Language service providers face increasing price pressure as well as growing demand from prospects and clients for post-edited machine translation (PEMT). If forces them to address the fundamental question of whether to implement MT in the workflows and . . .
 
MT’s Journey to the Enterprise
by Arle Lommel, Donald A. DePalma, May 17, 2016, 46 Pages View abstract 
As companies search for alternatives to deliver higher content volumes in more languages, machine translation (MT) is evolving from a niche solution for large enterprises into a mainstream option. Within a few years, CSA Research predicts that the majority . . .
 
The Calculus of Global Content
by Donald A. DePalma, May 17, 2016, 3 Pages View abstract 
Translation buyers and suppliers face the challenges of massive content volumes, along with demands for faster turnaround times and more target languages, all while dealing with flat budgets. Some look to machine translation as the solution. At the same . . .
 
LSP Use of Technology in 2015
by Benjamin B. Sargent, October 29, 2015, 5 Pages View abstract 
How do translation providers use the specialized technology of the industry in 2015? Where is each type of system most concentrated? This brief presents data on the use of translation memory (TM), translation management systems (TMS), and machine translation . . .
 
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