A quick overview of machine translation (MT) and post editing (PE) for localization service buyers and translators. Welocalize Language Tools team presents an overview of Concepts, Why MT?, Examples of Machine Translation and Post-Editing. Discussion of Post Editing and Light Post Editing. Additional topics include benefits of MT, MT patterns, output, raw MT. Automation, Language Services Provider. Contact Welocalize Language Tools for additional information. www.welocalize.com

1. Foundations
Machine Translation
Post-Editing
Copyright: Welocalize, Inc. 2014. All Rights Reserved

2. machine.translation
Copyright: Welocalize, Inc. 2014. All Rights Reserved

3. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
machine.translation
• Contracts
• Patents
• Annual Reports
• Light Marketing
• Software Documentation
• Software User Interface
• SEO (Search Engine Optimization)
• e-Learning Content
• User Guides
• Internal Corporate Communications
• Wikis
• Knowledge Bases
• Proposals / Draft Applications
• User Generated Content
Different use cases
for MT
(audience?
perishability?
visibility?)
Copyright: Welocalize, Inc. 2014. All Rights Reserved

4. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
why.mt
For clients
– Increase throughputs and consistency
– Reduce cost of translation
– Content explosion due to Internet
– Most internet content is in English (user community is global)
– Desire to translate also “lower quality” content, such as User Generated Content
(UGC) at a profitable price
– Quality of MT has improved (new technologies, lots of research)
For the translator
– Increase throughputs and consistency
– MT is likely to become commonplace, like TMs before
– More & more clients and LSPs use MT
– Be an early-adopter
– MT and new forms of post-editing requirements are fast evolving
Copyright: Welocalize, Inc. 2014. All Rights Reserved

5. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
basic.concepts
MT in a nutshell
[…] Machine Translation provides a set of tools by which digital text is automatically
translated from one language (e.g. English) into another language (e.g. Spanish).
Source: Systran user guide
There are 3 main types of MT systems with different underlying logics:
 Rules-based (RBMT)
 Statistical (SMT)
 Hybrid (SMT + RBMT)
Most systems used today are either statistical or hybrid. All system types can
be customized for specific clients, incorporating client Translation
Memories, basic preferences and/or terminology lists.
Copyright: Welocalize, Inc. 2014. All Rights Reserved

6. basic.concepts
Client-
specific data
TMs, glossaries
Domain-specific data
chemistry or mechanical or
IT or…
General language data
anything to“teach the system the
basics on the language pair“, so all of:
tourism, IT, automotive, literature,…
e.g. Google
Translate
and Bing
would be
Baseline only
Customizable
MT systems
(licensed or
open source)
Copyright: Welocalize, Inc. 2014. All Rights Reserved

7. - Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
basic.concepts
Understanding statistical MT
For the translator, it is important to understand that SMT systems are
based on algorithms calculating probabilities within a given set of data
(bilingual and monolingual).
In other words, the system learns from legacy human translations
(Translation Memories in our case) and calculates probabilities of most
likely translations from these, without applying linguistic rules as such.
Copyright: Welocalize, Inc. 2014. All Rights Reserved

8. basic.concepts
The logic behind
statistical
machine translation
(SMT)
Imagine the TM(s) as aligned data corpus – example
Example
Terminology
The term click appears > 16 000 times in TM A
In 90% of cases it is translated with fare clic
in 10% as: selezionare, scegliere, …
The probability is high, that the machine translation will be fare clic
…BUT, maybe…
The string click OK appears 500 times in TM A
In 50% of cases it is translated with fare clic su OK
in 50% as: selezionare OK
The probability is 50%, that the machine translation will be selezionare OK
Copyright: Welocalize, Inc. 2014. All Rights Reserved

9. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
typical.examples
good > perfect to overall understandable and fairly fluent
medium > contains useful chunks, terms and occasionally perfect output;
more or less understandable, little fluency
poor > poor with regard to understandability and fluency
 We carry out content evaluations to prevent content with overall poor
MT output from going into production
 Medium is the broadest category and can still lead to productivity
gains when used as a basis for post-editing
The quality of raw MT output can vary. A distinction is typically made as follows:
Copyright: Welocalize, Inc. 2014. All Rights Reserved

10. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
typical.examples
The quality of raw MT output can vary.
Example:
Copyright: Welocalize, Inc. 2014. All Rights Reserved

11. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
typical.examples
Know the patterns of MT output
Even ”good” MT output is not expected to be perfect. Depending on the underlying
MT logic and the language pair, there tend to be typical issues to fix, e.g.:
– issues around capitalization
– punctuation (source punctuation is copied)
– spacing
– omissions/additions of text (usually different in nature to those in fuzzy matches)
– unknown/new words may be translated literally or be left in English
– word order: can be mirroring the source
– compound formation
– word form agreement
→ being aware of typical issues helps good post-editing
Copyright: Welocalize, Inc. 2014. All Rights Reserved

12. typical.examples
Copyright: Welocalize, Inc. 2014. All Rights Reserved

13. typical.examples
Copyright: Welocalize, Inc. 2014. All Rights Reserved

14. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
post.editing
What is Post-Editing?
Copyright: Welocalize, Inc. 2014. All Rights Reserved

15. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
post.editing
In other words…
 Make changes where necessary, using as much of the MT output as possible
(based on language and client requirements)
 Read the MT output & the source > decide quickly what can be used
 Use as many “bits/sections“ of the MT output as possible:
move them around, correct word forms, change the part of speech, use them as
inspiration
 Look up key terms in your reference material as usual, but also learn to trust the
customized output
 Automate with customized QA checks
Adjust your expectations. Rethink your approach. Report recurring errors.
Copyright: Welocalize, Inc. 2014. All Rights Reserved

16. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
full.post.editing
full post-editing: “publishable quality”
► Client Glossary, TM, Style Guide and others apply
Examples:
 infinitive / imperative preferences?
 passive / impassive preferences?
 formal / informal preferences?
 different styles for headers, lists, tables?
 special formatting of UI options? (bilingual, English)
 are measurements to be converted?
 Terminology
If the client requests “full post-editing”, this means publishable quality.
The post-editor is responsible for ensuring the client requirements with regard
to final quality expectations are met.
Copyright: Welocalize, Inc. 2014. All Rights Reserved

17. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
light.post.editing
light post-editing / “understandable quality”
Full Post-Editing Light Post-Editing
Grammar and spell-checking are correct Minor issues in grammar (and spelling) are
acceptable
Terminology is accurate & consistent Terminology is understandable and
actionable
Spelling is consistent (e.g. hyphenation) Variations in spelling are acceptable
Style is consistent (headers, list items,…) Style variations are acceptable
Punctuation is correct Variations/errors in punctuation are
acceptable
Style & tone are appropriate for content Style & tone are not offensive
Specific requirements: 33 cm (13‘‘); change
EN quotation marks to FR/DE/….
Follow MT output, e.g. keep proposed
number format 13‘‘ (33cm), English
quotation marks,...
… …
Copyright: Welocalize, Inc. 2014. All Rights Reserved

18. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
post.editing
light post-editing versus full post-editing
*Copyright CSA
Copyright: Welocalize, Inc. 2014. All Rights Reserved
Image © Common Sense Advisory, “Post-Edited machine translation defined”, April 30, 2013

19. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
post.editing
Notes on productivity
Just as with human translation, throughput can vary and depends on:
– language pair
– content type & complexity
– experience
– domain knowledge
– quality requirements
– use of automatic QA tools
– quality of TM and reference material
With MT, additional factors are:
– quality of the MT
– experience with post-editing
Compared to average daily throughputs for human translation, average daily
throughputs for full post-editing can be up to 3 x higher.
Copyright: Welocalize, Inc. 2014. All Rights Reserved

20. Subheader
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
- Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
take.aways
 There are different use-cases of MT associated with different levels of
final (post-edited) quality
 When full PE is requested, this means publishable quality
 There are different MT systems, Welocalize works with a range of them
 MT output varies in quality, we evaluate it with our translation partners
to ensure the necessary quality for post-editing is met
 MT is not expected to be perfect, that„s why we need post-editors!
 Post-editing replaces the translation stage in the workflow, but it is a
different task, cognitively
 MT systems can improve through adding more data & through
constructive feedback from post-editors
Copyright: Welocalize, Inc. 2014. All Rights Reserved

21. - Sample text here sample text here Sample text here.
- Sample text here sample text here Sample text here.
Sample text here Sample text here Sample text here Sample text
here Sample text here Sample text here.
trademark.disclaimer:
Product names, logos, brands and other trademarks referenced within this
presentation are the property of their respective trademark holders. These
trademark holders are not owned or affiliated to Welocalize, Inc., our
products, or our website. They do not sponsor or endorse our materials.
Reference is for education purposes only.
Copyright: Welocalize, Inc. 2014. All Rights Reserved

22. Questions?
Contact the Welocalize Language Tools Team
lena.marg@welocalize.com, elaine.ocurran@welocalize.com
Welocalize
Frederick, Maryland - Headquarters
241 East 4th St. Suite 207
Frederick, Maryland 21701 USA
[t] +1.301.668.0330
[t] +1.800.370.9515 Toll Free
www.welocalize.com
Copyright: Welocalize, Inc. 2014. All Rights Reserved