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Introduction
Translation Quality Assurance software
(hereinafter referred to as TQA tools)
compares source and target segments of bilingual texts (saved in .doc, .rtf,
and .ttx files) in order to detect translation errors. Such errors include:
inconsistencies; terms, which have not been translated in accordance with a
project glossary; omissions; target segments, which are identical to source
segments; punctuation, capitalization and number value/formatting errors; and
incorrect untranslatables and tags.
The aim of this study is to compare three of
the most popular TQA tools in order to find out their strengths and
weaknesses, and therefore help translators, project managers and proofreaders
to select the optimal TQA tool for any particular job.
Intrinsic Limitations of TQA Tools
There are a number of intrinsic limitations
with TQA tools, some of which are listed below.
- TQA
tools cannot detect mistakes arising from an incorrect (or incomplete)
understanding of the source text, poor stylistics or an inappropriate
choice of language register.
- When
TQA tools check terminology, they are limited by the glossary being used
for the check.
- TQA
tools often detect false errors because they do not ‘understand’ that
source and target languages may have different grammatical rules (for
example, punctuation and capitalization). As will be seen below, the only TQA tool which has
different language settings is QA Distiller.
- Comparison
tools expect the source text to be correct, which is not always the
case. If the translator rectifies a mistake in a source sentence (such
as incorrect initial capitalization or punctuation), this may result in
a false error being detected by the TQA tool.
- TQA tools work on the logic
that all inconsistencies are equally bad. However, IMHO only special
terminology should be translated consistently while general phrases
which are identical in the source text may be translated in different
ways in order to improve readability and style. Identical phrases may
even require different translations depending on context.
General Description and Features of Three TQA
Tools
The three
TQA tools tested in this study were: SDL Trados Terminology Verifier and QA
Checker; Wordfast Quality Check feature; and QA Distiller (hereinafter
referred to as Trados, WF and QAD, respectively).
General information about these three tools is contained in
Table 1 below and a comparative list of their main features is given in
Table 2.
Table 1
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Trados
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Developed
by SDL.
Plug-ins
integrated in Trados TagEditor.
Files
that can be checked directly: ttx.
User
interface used: TagEditor.
Protection:
soft key license file.
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WF
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Developed
by Yves Champollion.
A
feature integrated in WF.
Files
that can be checked directly: doc, rtf.
User
interface used: MS Word.
Protection:
license code.
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QAD
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Developed
by Yamagata Europe (Belgium).
A stand-alone
application. Requires installation of Trados.
Files
that can be checked directly: rtf, ttx, tmx.
User
interface used: proprietary (QAD UI).
Protection:
license code (requires Internet protection and the license only works for
eight hours after disconnecting from the Internet).
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Table 2
X means
that a feature is provided.
0 means
that a feature is not provided.
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Name
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Details and explanation of the check
carried out
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Trados
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WF
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QAD
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Terminology
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Target terms
used are identical to those specified in your glossary.
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X
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X
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X
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Segment
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Forgotten and empty translations.
Identical source and target text.
Target segments that are shorter or longer
than the source by a specified percentage.
Target segments that contain more than a
specified number of characters.
Target segments that contain forbidden
characters.
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X
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0
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X
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Inconsistency
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Repeated phrases translated inconsistently.
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X
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0
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X
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Punctuation
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Different end of sentence punctuation in
source and target segments.
Spaces before punctuation.
Double spaces.
Double dots.
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X
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Only
double spaces
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X
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Capitalization
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Capitalization
of initial words.
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X
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0
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X
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Numbers
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Are numbers
identical in source and target segments.
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X
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X
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X
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Tags
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Are
tags identical in source and target segments.
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X
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X
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0
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Untranslatables
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Automatically
detects untranslatables (even those not included in your glossary) and checks
whether they are identical in source and target segments.
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0
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X
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0
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Bookmarks
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Source
and target texts contain an identical number of bookmarks.
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0
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X
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0
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Other Features
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Trados
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WF
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QAD
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TQA
check settings can be customized.
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X
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X
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X
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Customized
TQA settings can be saved to file.
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X
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X
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X
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The
results of a TQA check can be save in a log file.
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X
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X
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X
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Checks
are performed in real time during the translation session (not after
translation is completed).
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0
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X
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0
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Batch
mode (the TQA tool can check multiple files during a single operation).
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0
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X
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X
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Indication
of segment with detected error
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X
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X
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X
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Possibility
to add your own TQA checks (macros)
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0
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X
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0
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Fuzzy
terminology checks (the TQA takes into account during the terminology check
that words may have various forms (case endings, for example)).
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Õ
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X
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X
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Language
dependent settings.
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0
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0
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X
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License
price (price of one user license).
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$895.00
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From
ˆ90.00
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$1000.00
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Technical
support from the developers.
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X
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X
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X
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Detection of formal errors
In order
to test these TQA tools, I created a test .doc file (1,373 words) containing a
sample source text from a real client (Volvo Cars), and translated it with
Trados in both MS Word and TagEditor. As a result, I had two identical
bilingual target files (1,071 words) saved in .rtf and .ttx formats.
At the
first stage (check of formal errors only) I added seven typical formal errors
to both files:
1. One
sentence was kept in English (identical source and target segments).
2. A
double space.
3. One
end of sentence punctuation different from that in the source sentence.
4.
Repeated phrases translated inconsistently.
5.
Incorrect untranslatable (Volvo S60 in the source segment changed to Volvo
S60R in the target segment).
6.
Incorrect number (350 in the source segment changed to 360 in the target
segment).
7. One
closing round bracket ")" missing in the target segment.
All
special terminology in the target file was translated in accordance with my
Volvo glossary (although I did not perform a terminology check at this stage
of the study).
The
settings in the three TQA tools were optimized experimentally to ensure
detection of the maximum number of real errors and the minimum number of
false errors (maximum ‘signal to noise’ ratio).
The
results of the TQA formal error check are given in Table 3 below.
Table 3
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Total
number of errors detected
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Number
of real errors detected
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Number
of false error reports
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Number
of real errors not detected
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Trados
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11
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6 of 7
(all except #5)
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5
(mostly such as ‘100’ translated as ‘ñòî’)
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1 (#5,
untranslatable)
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WF
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11
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3 of 7
(##2,5,6)
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8
(mostly such as ‘110km/h’ translated as ‘110 êì/÷àñ’)
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4 (##
1, 3, 4, 7)
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QAD
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20
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6 of 7
(all except #5)
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14 (all
were number errors)
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1 (#5,
untranslatable)
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As a
result of carrying out this formal error check, the conclusions listed below
in Table 4 can be drawn.
Table 4
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Tool
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Strengths
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Weaknesses
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Trados
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Detects
the majority of real formal errors (6 of 7).
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Checks
only bilingual .ttx files (does not check .doc and .rtf files directly).
Does
not detect errors in untranslatables (if they have not been included
manually into the glossary).
Not
user-friendly.
Learning
curve is long.
High
number of false errors mostly associated with numbers translated by words
(‘100’ translated as ‘ñòî’)
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WF
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Highest
user-friendliness.
Learning
curve is very short.
The
only TQA tool which automatically detects incorrect untranslables not
included in the glossary.
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Detected
only 3 of 7 errors.
Does
not detect real errors such as ## 1, 3, 4, 7.
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QAD
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Detects
the majority of real formal errors (6 of 7).
Batch
mode enables translation companies to check many files at a click.
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Failed
to install on my desktop with Russian version of Windows XP (license code
field was not displayed), but did install successfully on my notebook with
the same OS.
Verifies
its own license code via an Internet connection and only works for eight
hours after disconnecting from the Internet.
Detects
many false errors (14, mostly number values and formatting).
Does
not detect errors in untranslatables (if they have not been included
manually into the glossary).
Not
user-friendly.
Learning
curve is long (compared to WF).
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Detection of Terminology Errors
In order
to test the terminology check features, I added four terminology errors to
the test translation. First, I
translated ‘simulator’ as ‘èìèòàòîð’, rather than ‘ñèìóëÿòîð’, then I created glossaries containing one record only (simulator
> ñèìóëÿòîð) in
the formats required by each TQA tool.
Note:
Russian is an inflected language and my test translation contained various
forms of the word ‘èìèòàòîð’.
The
results of terminology check were as follows:
Table 5
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Total
number of errors detected
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Number
of real errors detected
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Number
of false errors reports
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Number
of real errors not detected
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Trados
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6
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4
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2
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0
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WF
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4 of 4
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4
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0
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0
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QAD
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no data
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no data
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no data
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no data
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Comments
on the data received:
Trados - The false errors detected by
Trados were caused by fuzzy matches.
On both occasions, Trados suggested the use of the glossary term
‘simulator/ñèìóëÿòîð’
for the verb ‘simulate’. The user has no control over such situations. The
only option is to ignore such false errors.
WF - This proved to be the most simple,
accurate, user-friendly and controllable terminology checker. The user can
set the level of fuzziness by using wildcards.
QAD - The copy of QAD installed on my
notebook failed to perform the terminology check. During the Analyze step, the application
returned the following error message: “A program exception occurred”.
Are TQA Tools Necessary for an Experienced
and Diligent Translator?
As a
freelance English-Russian translator with 27 years of experience, I always
take pride in my human quality assurance methods. I proofread all my
translations at least twice before delivery and frequently hire a proofreader
or a technical expert to check my translations. Further information about my
human quality assurance methods can be found on my website at www.erussiantranslations.com/Article9.htm.
Since
2000, I have translated about 700,000 words per year, and in the ten years
before that I translated 56 novels. My sample translations were checked and
approved by ATA, ITI and UTR. My clients are always happy with the quality of
my translations.
However,
are experience and human quality assurance methods enough to avoid formal and
terminology mistakes? To find the answer I checked a 10,000-word translation
I did in 2005, before I started to use TQA tools. I found two terminology and
eight formal errors, which is enough to suggest that TQA tools may be as
useful for experienced translators as they are for beginners.
Conclusions
1. TQA tools do not replace human
editors/proofreaders, but only help them. First and foremost they help
translators.
2. Each of the three TQA tools has
its own strengths and weaknesses, as well as its preferable area of use.
- Trados is a good choice when you need to check
.ttx files. Besides due to aggressive marketing Trados is a de facto
industry standard.
- WF provides the best check of terminology
and untranslatables.
Furthermore, the WF developer offers the best technical support
to users. The program is, in my
opinion, the optimum choice for price-sensitive freelancers who do not
want to spend many hours learning to use a complex software.
- QAD is the only tool enabling you to check
Translation Memories saved in .tmx format and to use language dependent
settings. Unlike current version of SDL Trados, QAD operates in batch
mode (checks many files at a click) which is a big advantage for
translation companies/agencies. Therefore QAD is probably the best
choice for corporate users.
3. No matter how experienced the
translator is and what human quality assurance methods s/he uses, TQA tools
are able to decrease the number of mistakes and improve the overall quality
of translation.
The
results given above were achieved on my two PCs, a desktop and a notebook,
both running the Russian version of Windows XP with SP and updates. Were the
tests to be run on computers using a different operating system, there might
be a slight variance in the results.
I would
like to record my special thanks to Nathalie De Sutter for her invaluable
contribution to this study.
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