1. Introduction
Methodology
Discussion
Integrating Machine Translation with Translation
Memory: A Practical Approach
Panagiotis Kanavos and Dimitrios Kartsaklis
November 4, 2010
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2. Introduction
Methodology
Discussion
Introduction
Despite the ongoing research and the progress on the field,
Machine Translation has not been widely accepted by the
professional translation industry
Common criticisms:
MT is only suitable for draft translations of e-mails and web
pages
MT is not efficient for morphologically rich languages
MT is useful only to large companies owning a wealth of
resources
In a nutshell: MT is something for researchers to play around
with
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3. Introduction
Methodology
Discussion
A Case Study
How MT can be incorporated into professional translation
workflows, with limited resources, in ways that significantly
increase productivity.
We combine both statistical and rule-based MT systems with
Translation Memory software using two approaches:
The on demand, sentence-by-sentence application of MT
The one-time application of MT into the whole translation
project
The case study is conducted in production conditions, with
final deliverables that require the highest translation quality.
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4. Introduction Configuration
Methodology Segment-by-segment workflows
Discussion One-time MT application workflow
Our setting
Language pair: English to Greek
Text to be translated: Two Informatics books: one
technical guide and one academic textbook.
TM size: 140,000 TUs coming from in-domain texts
Terminology DB size: 30,000 entries
Fuzzy threshold: 70%
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5. Introduction Configuration
Methodology Segment-by-segment workflows
Discussion One-time MT application workflow
Software programs and combinations
MT systems:
Statistical: Moses
Rule-based: Systran
CAT programs:
Swordfish II (Java application) over Linux
D´j` Vu X over MS Windows
ea
Wordfast, an MS Word macro template
Three combinations, based on practical factors:
Sentence-by-sentence workflow with Swordfish/Moses
Sentence-by-sentence workflow with Wordfast/Systran
One-time MT application workflow with D´j` Vu X/Moses
ea
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6. Introduction Configuration
Methodology Segment-by-segment workflows
Discussion One-time MT application workflow
Swordfish/Moses combination
Swordfish: Allows connection to external programs or scripts
Connection with Moses achieved with a custom Python script
Basic workflow:
if TM match > 80% then
accept fuzzy match for post-edit
else if 70% < TM match =< 80% then
evaluate the fuzzy match
if quality not acceptable then
apply MT
end if
else
apply MT
if quality not acceptable then
type the translation from scratch
end if
end if
post-edit
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7. Introduction Configuration
Methodology Segment-by-segment workflows
Discussion One-time MT application workflow
Swordfish/Moses combination: Results
Book 1 : Instructive guide, Book 2 : Textbook
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8. Introduction Configuration
Methodology Segment-by-segment workflows
Discussion One-time MT application workflow
Wordfast/Systran combination
Wordfast: A macro template working on top of MS Word
Great deal of customization through MS Word macros
Rule-based version of Systran, supporting user dictionaries
Basic workflow:
if TM match < 70% then
apply pre-editing macros
send segment to MT engine
apply post-editing macros
while MT result not good do
amend Systran user dictionary and re-send segment to MT
end while
else
accept the translation for post-edit
end if
post-edit
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9. Introduction Configuration
Methodology Segment-by-segment workflows
Discussion One-time MT application workflow
Wordfast/Systran combination: Results
Book 1 : Instructive guide, Book 2 : Textbook
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10. Introduction Configuration
Methodology Segment-by-segment workflows
Discussion One-time MT application workflow
D´j` Vu X/Moses combination
ea
D´j` Vu X: similar concept to Swordfish
ea
However: No way of integration with an MT system, so the
only option is pre-translation of the whole project with Moses
Send for MT only segments with no TM matches or TM
matches below 80%
Pre-translation stage:
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11. Introduction Configuration
Methodology Segment-by-segment workflows
Discussion One-time MT application workflow
D´j` Vu X/Moses combination
ea
Basic workflow:
if TM match > 80% then
accept the translation for post-edit
else
evaluate MT translation
if quality not acceptable then
if any TM match exists (between 70-80%) then
accept the translation for post-edit
else
apply “auto-assemble” feature
if quality not acceptable then
type the translation from scratch
end if
end if
end if
end if
post-edit
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12. Introduction Configuration
Methodology Segment-by-segment workflows
Discussion One-time MT application workflow
D´j` Vu X/Moses combination: Results
ea
Book 1 : Instructive guide, Book 2 : Textbook
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13. Introduction
Methodology
Discussion
Productivity increase
MT & TM combination: Productivity increased to a level not
possible by applying either technology in isolation:
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14. Introduction
Methodology
Discussion
Important factors
Quantity and quality of TM entries
The domain of the translation material used to train the
statistical MT system
The above impose serious limitations for those who work with
small texts in many different domains. Rule-based systems are
more suitable in such cases
Language pair: Coding efficient user dictionaries with
morphologically rich languages is difficult and requires some
trial and error. Phrase-based systems like Moses have better
performance
Style of text: Productivity is higher with repetitive text and
step-by-step instructions
User expertise with all technologies involved
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15. Introduction
Methodology
Discussion
A proposal for a unified application
For general acceptance by the professional translation
community, MT should be integrated with TM into an
intuitive unified system
Basically a TM environment, with the MT engine as an extra
component working on top of it
MT suggestions should be presented in a controlled and
selective way
Basic components:
A 2-column translation grid for source and target segments
Terminology management
MT engine
Alignment tool
Quality assurance control
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16. Introduction
Methodology
Discussion
Advanced issues
Automation of the training process with TM databases
Statistical systems require considerable computing resources.
A solution: MT as Software As a Service (SaaS)
Terminology databases can be used for more than reference
purposes
Additional entry fields for coding MT dictionary entries
(Systran)
Linguistic information can be used for creating factored models
(Moses)
Automatic suggestions-as-you-type (TransType, Caitra)
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17. Introduction
Methodology
Discussion
Summary
The combination of MT with TM results in significant
productivity increase not feasible in a TM-only environment
Currently there is not a straightforward way for doing that
Work is in progress by the authors towards this purpose, in
the form of a Software Specification document that will
describe the design and the components of such a system in
every detail
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18. Introduction
Methodology
Discussion
Thank you!
Any questions?
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