Savings of 83% thanks to CAT tools... [case study]

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Case study
Savings of 83%
thanks to CAT tools...

The challenge
Tradas makes strategic use of computer-aided translation tools (CAT) so that it is able to
offer its customers the best possible management of their multilingual content.
This report presents a detailed case study showing what makes CAT tools interesting - and
context matching functionality in particular – and the gains they represented in terms of
turnaround times and costs of production for multilingual material for one of our customers,
a multinational manufacturer of industrial machinery.
The context
Computer-assisted translation tools (CAT) are software programs that make it possible to
rationalize your translation projects in a secure and effective way, in terms of not only the
linguistic and financial aspects but also the timing. They have kept in step with technological
capabilities and with the needs of the users.
However, it is always important to make a clear distinction between CAT and machine
translation (MT), which is done using software such as Google Translate or Babylon; MT
suggests translations based on banks of predefined words and phrases, generally producing
a rather literal and extremely rough translation.
Computer-aided (or computer-assisted) translation, however, is all about the input from a
human being, who is doing the translation work with the help of specialist computer
software. It makes the task easier thanks to features allowing the linguistic content to be
handled using text segments that have previously been translated and/or reference material
that can be imported into the system to provide the linguistic context for creating appropriate
and extremely accurate output.
Translation memories
The documents are processed through these tools in order to simplify the translation process
and make it more watertight, using terminological content that has been translated

consistently earlier. Or to put it another way, the content of a document is broken down into
syntactical elements called ‘segments’ that are translated and then saved as source/target
pairs in a database known as a ‘translation memory’. This is then available for new
translations, which helps to optimize the translation process.
Context matches
The tool detects similarities between the segments in the document and the ones stored in the
translation memories. Whenever a new segment is found, the tool suggests a number of
translations that the translator could use, depending on the type of match.
Depending on how similar the segments are, different types of ‘matches’ can be generated:
exact matches, which are referred to as ‘100% matches’, and those that are less precisely
matched, known as ‘fuzzy matches’ or ‘fuzzies’.
However, there is also a type of match for which the degree of certainty is in some ways
even better than for an isolated 100% match. These are known as ‘context matches’. The
system does more than merely analysing the correspondence between the segment and the
translation memory content: it can also look at the match levels for the segments before and
after the segment in question, so that it is also taking account of the surrounding context.
***As well as 100% matches, internal repetitions, fuzzy matches and context matches, there
is also another type of match known as a ‘perfect match’. This type of match is used for texts
that have only been slightly amended. The CAT takes account of a previously translated
bilingual text, comparing the new source document that is to be translated against the
bilingual original. The context is also analysed in this case, but then with respect to the
previous version instead of checking it against the translation memory. ***
The opportunity
CAT tool performance is improving as time progresses, going further and further in terms of
the matches that it is able to recognize, particularly since Studio 2009 was released
(software supplied by SDL Trados, the market leader). The tools analyse the context in which
the translatable segments appear, looking for similarities in the translation memories so that

they can then suggest texts to be inserted automatically. This method offers translation quality
that is unprecedented.
Having said that, however, applying this technique does require translation memories that
are up-to-date and accurate: results may otherwise be disappointing in terms of
quality. Having the contents of the translation memories validated by the customer
beforehand is therefore essential, even if this may demand quite an effort if the volumes
involved are large. This is why we collect customer feedback after every translation project to
update the reference materials.
We have therefore made a case study of how these features are changing and how they can
be used. We pay a great deal of attention to adherence to our procedures and we
rigorously monitor the quality of the work we deliver. Moreover, in addition to the
considerations listed above, we are well aware that the concept of profitability is a strategic
aspect that everyone wants to optimize. The impacts in terms of cost reduction and
throughput times are exactly why it made sense for our client to use this technology in the
production of multilingual manuals. The constraints associated with the implementation of
this functionality had to be offset by genuine benefits in order to make the whole approach
viable.
Justification
In addition to the opportunities inherent in reducing production times and the costs for
multilingual management of their documentation, there were two further elements that came
to justify the benefits of this way of working:
• manuals for different machines often had several chapters that were similar
• the number of languages to be handled: 25
We translate a certain number of manuals a month into various European languages. That is
one item that will help justify such an operation. In effect, given the number of languages,
any optimization achieved in one language will be multiplied up that number of times, which
means a substantial benefit.
The second justification is in terms of the content of their documentation. The chapters of the
manuals exhibit some significant similarities. The advantage in this case can be found in the
reduction in the numbers of errors and in creating a translation ‘production line’. In
particular, this makes it possible to guarantee perfect consistency in the terminology across
all their manuals and indeed the documentation in general.

Rolling out the process
Preliminary test of the functionality
Caution was in order, so it had to be done step by step. In this case, the test focused on two
of the twenty-five languages that were to be handled.
Using the context matching techniques explained above, it is possible to literally lock
segments in place. For this work, you are then in a “machine translation” type configuration,
in which the segments can be suggested by the translation software and where the translator
only has to post-edit the automated output document. This means that the translator does not
have to be involved in the translation of that specific segment. And that is precisely the
advantage of this technique: it lets you reduce the numbers of words, phrases and segments
to be translated, thereby cutting down on the time needed for translating them. It is a
solution that is only acceptable once the software is capable of generating translations that
are good enough every time.
This is why we started a pilot phase project for the two Western European languages that
were most important for the customer and which do not present too many major issues (such
as declensions, for instance), namely Dutch and French.
Updating segments
And now the constraints. This technique requires translation memories to be used that are
consistently kept updated with the client’s terminology, without which we would not be able
to benefit from the expected improvements, with the counter-productive consequences that
this would entail.
As we have already been working for this customer for twenty years, we knew that the
memories did contain outdated and obsolete segments. Part of their linguistic material was
no longer being utilized because it was about machines that were obsolete and were
therefore no longer referred to in their technical literature. Updating the translation memories
was greatly facilitated thanks to excellent CAT tools management. Clearly, the creation of
translation memories is based on a classification that is specific to the client and which has to
be properly documented. This is what allowed us to update their translation memories, only
retaining the appropriate documentation and terminology and applying filters to improve the
accuracy of their terminology searches.

Finally, we conducted a test on the new material that was to be translated in order to
validate the context matches before locking them and to ensure the expected level of quality.
The tests were concluded successfully, marking the end of the preparatory technical phase of
the operation.
During the pilot phase, all translation requests were handled in parallel, using the old
memories and the new ones. The results obtained were then compared by measuring
performance criteria that had been selected to help decide whether or not this technique
should be used once the pilot phase was complete.
Conclusion
The other twenty-three languages then went through the same procedure. Although the
results obtained were not identical for all languages, they were still enough to demonstrate
clearly that using this technical procedure gave genuine benefits.
For translation of a similar manual to those translated before, the gains that the context
matching functionality yielded were over 38%.
Here are the figures:
Translation Costs (Euros) Savings
Without CAT 10,418.46 0%
Using CAT without Context Matching 2,948.60 72%
Using CAT and Context Matching 1,821.00 83%
This is a superb result, achieved without compromising the quality of the translations.
This case shows nicely how important it is to use computer-assisted translation tools
strategically, following the technological advances in order to make the most of the
opportunities they offer, while nevertheless quantifying the risks associated with them.

This let us rationalize our client’s translation projects in a secure and effective way, in terms
of not only the linguistic and financial aspects but also the timing.
The table below shows the delivery deadlines according to the use of CAT tools or not.
Volume
(avg: 250 words/p.)
Lead time
Normal
Lead time
CAT tools
50 pages 6 working days 5 working days
Standard time without using CAT tools
The average capacity for a translator varies from 2500 to 3000 words per working day
once the translation process is triggered.
Lead time using CAT tools
Translation tools speed up the translation process (daily capacity of 3000 to 4000 words)
following the degree of repetitive match types.
The operation as a whole was able to strengthen a relationship that had already been in
place for over twenty years. Analysing and updating our expertise in computer-assisted
translation tools is a key part of our efforts to provide customized services and to optimize
the management of multilingual content.

Thanks for reading!
Special thanks to Veerle, Elyas and Gauthier for their contribution to the writing and
reviewing of this case study.
For more information about Tradas and its team, contact
Krista Suys, Customer Service Director.
+32 (0)2 340 94 50
customerservice@tradas.com

Savings of 83% thanks to CAT tools... [case study]

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