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| The Business Case for Machine Translation |
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| Keywords |
machine translation, automated translation, zero translation, terminology
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Abstract |
Machine translation is technology that many organizations will find
indispensable in removing the language obstacle for international operations or
domestic multilingual applications. For this report, we interviewed 31 users of
machine translation and eight LSPs offering MT to understand what drove them to
the technology, how well it's met their needs, and what they plan to do next.
- In the Vox Populi section, we review the findings of our in-depth interviews
with organizations using machine translation technology. We spoke with users
with MT installed behind the firewall, accessing it via SaaS, and relying on
LSPs to do the work.
- In the Analysis section, we address issues of process, choice of technology,
justification, and approach to machine translation.
- Looking forward in the Impact section, we discuss changes that both buyers
and suppliers will make to improve the technology and its
deployment.
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Benefits |
For Buyers:
- This report lays out the concerns that went into building the business case
for using machine translation at 27 corporations, two government agencies, and
two non-governmental organizations. It will save you time by focusing your
planning on the issues that drive the usage of machine translation.
- It describes the applications for which machine translation is suitable, the
types of content for which they are using machine translation, the requirements
for using MT software, and the differences between the major technologies.
For Suppliers:
- This report discusses the opportunity for LSPs to use machine translation to
increase productivity.
- It identifies the situations where clients are already asking their LSPs to
investigate or use MT for their high-volume, quick-turnaround applications.
- It outlines th pain points that drives buyers to consider machine
translation as part of their global information strategy.
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Physical Details |
Authors: Donald A. DePalma and Nataly Kelly
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Date: 20 August 2009
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ISBN: 978-1-933555-68-3
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Pages: 37
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Companies |
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AppTek, Asia Online, Babel Fish, Babylon, Ciyasoft, Freetranslations, Google,
IBM, Language Lens, Language Weaver, Lionbridge, Lucy Software, Microsoft,
Moses, MultiCorpora, N-Stein, PROMT, Sakhr, SDL, Softissimo, SYSTRAN |
Table of Contents |
Topic
Structure of this Report
Past Common Sense Advisory Research on Machine
Translation
Vox Populi
Why Organizations Opt for Machine Translation
Daunting Volumes Steer Organizations toward
MT
Quick Automated Translation Turnaround Attracts Buyers
Some
Implementers Cite More Web Traffic as the End Goal
Cost Comes into Play, but
Rarely in Isolation
How Organizations Decide When to Use Automated
Translation
The Business Case Reveals the Language Selection
Sweet Spot
Machine Translation Suits Some Content Types Better than
Others
The Tipping Point for When MT Makes Sense Varies by
Application
Several Factors Drive Choice of Automated Translation
Solutions
Deployment Options: Take Your Pick
Technology
Type: Sometimes a Philosophical Question
The Quality Question: How Good
is Good Enough?
Achieving the “Right” Level of Quality Means More
than Just MT
Punching Up Quality to Human Translation
Levels
Conclusions from Our Interviews with Machine Translation
Users
To Make the Business Case for MT, Do the
Math
Which Type of Automated Translation Makes Sense for You?
Raw Output: Fast and Cheap Insight for
Information Consumers
Post-Processed MT: Faster, Cheaper, No-Excuses
Translation
Total Automation: Overcoming Information Deficits in Other
Languages
Calculating and “Selling” the ROI of Fully Automated
Translation
Building Consensus for the Strategic Decision to
Use MT
Improving Machine Translation Quality: Trust but
Verify
Choosing an MT Technology – for Now and the Long Run
Picking a Business Partner for Machine
Translation
Choosing the Right Technology for Automated
Translation
Broaden the Demand inside Your
Organization
Impact
Glossary
Glossary of Terms Relating to Machine
Translation
Figure 1: Organizations Target Share of World
Online Wallet (WOW)
Figure 2: Tipping Points for When Machine Translation
Makes Sense
Figure 3: Linguistic Assets and Human Intervention Improve MT
Output
Figure 4: Corporate Content Sources and Destinations
Figure 5:
Process Continuum of Machine Processing of a Translation Unit (TU)
Figure 6:
How Ongoing Machine Translation Compares to Human Efforts
Tables
Table 1: How Organizations Use Machine
Translation
Table 2: Rules-Based, Statistical, and Hybrid MT
Solutions
Table 3: Where to Expect MT Improvements from 2010 to
2015
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