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Computer-Assisted Translation Tools

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Trados Studio initial version 2014 threw out SDL’s traditional WinAlign

alignment tool and now offers an integrated alignment tool that offers an

option called "alignment quality value." This is a setting that allows the user to

adjust the confidence level of the match. For example, if you have set a high

quality level and there is a huge difference in the number of words in the

source and target segments or there are numbers in the source but not in the

target, the translation units would be rejected.

Figure 174: Trados 2014 alignment tool with adjustable quality value

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In later editions of version 2014 and version 2015, SDL reverted to the old

way of reviewing the alignment first before you send the data to the

translation memory (unless you choose Align Multiple Files—even if it’s just

a single file pair—and then select Save alignment result files for later

review).

Figure 175: Alignment in SDL Trados Studio 2015

With all these difficulties, why would alignment still be a helpful process?

Alignment can be a very powerful tool if you have specific sets of already-

translated documents that correspond closely to new documents that now

have to be translated. The amount of time you can save and the level of

consistency and quality you can achieve by aligning the existing documents

and using that as the basis for your translation can be immense, and there’s

simply no reason not to go that route. But for other documents, unless you

can hire someone else to do mass alignment of existing materials (someone

I think the over-enthusiastic embrace of the "new and better alignment" that

was supposedly good enough to not even be reviewed anymore and the

relatively quick reversal is a good illustration that some tricky problems in the

translation realm are not necessarily solved just by throwing more technology at

them. Unless there truly is a new paradigm in alignment, just making it a little

better still doesn’t necessarily mean it’s good enough.

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with the odd combination of being both cheap and well-qualified . . .), I would

strongly advise you to build up your translation memory database by simply

performing translation in the tool of your choice and adding material to your

translation memory segment by segment.

Now . . . there is also a less "traditional" form of alignment, which in most

situations will be more successful.

Terminotix’s AlignFactory (see www.terminotix.com) offers an uncommonly

high accuracy of alignment because a) it uses a highly sophisticated alignment

engine and b) it uses a number of filters that filter out any unlikely match (for

instance, based on differing lengths of segments).

Figure 176: AlignFactory’s filter settings for alignment

Furthermore, with AlignFactory you can also select thousands of file pairs

(including PDF files), have them matched up (they have to follow certain

naming conventions such as a language identifier), and then have them

aligned in one big swoosh. And it really is one big swoosh: the speed of the

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alignment is mind-boggling. In fact, it’s so fast that I have repeatedly thought

that something had gone wrong only to find that it had already successfully

completed the alignment. While it’s not perfect, it certainly has brought

alignment to a different level.

Figure 177: AlignFactory’s alignment results—note the correct alignment of the first segment

Another alignment tool that works somewhat differently is LF Aligner (see

sourceforge.net/projects/aligner). What’s different about this tool is a

linguistic backbone in the form of dictionaries in a total of 32 languages (all

the EU official languages except Irish Gaelic and Maltese, as well as Croatian,

Arabic, Hebrew, Chinese, Japanese, Korean, Latin, Norwegian, Russian,

Swahili and Turkish), permitting the automatic generation of dictionaries in

any combination of these languages. It’s on the basis of those dictionaries

that the tool decides how to match segment pairs.

YouAlign (see www.youalign.com) is a free service that uses the same alignment

engine. While there are limitations to file size and number of files you can align,

it should give you a good idea of what AlignFactory can do.

Also, there is Similis (see page 265), which aligns at a much greater accuracy

than traditional tools because of its underlying data. Unfortunately, Similis only

works in seven European languages and the alignment process is excruciatingly slow.

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The supported file formats include TXT, DOC(X), RTF, HTML, TMX and (in a

limited fashion) PDF. Or you can download EU documents or other online

documents for alignment purposes directly from within the tool.

Figure 178: Format options in LF Aligner

Another tool that has started to use (and improve) the logic of LF Aligner is

XTM. Its development team acquired very comprehensive lexicon data in 50

language pairs as the basis of their alignment, resulting not only in better

alignment accuracy but also the ability to give a relatively reliable "confidence

score" for every proposed segment match. The segment pairs are output in

two different Excel tables (I know, not my favorite tool for this kind of task

either...). One file lists all segment pairs with at least 90% confidence (the

confidence is assigned on the basis of the lexicon data, as well as items like

function words, numerals, named entities, and of course punctuation), and

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another file contains every segment pair and therefore needs further editing.

So, if you’re in a hurry, you just take the "good" file and import that into a TM;

if you really want to squeeze everything out of a text, you take the file that

might need some work.

Figure 179: XTM’s alignment results with the confidence rating ("Probability"). Note that the

first segment here is correctly aligned.

. . . Back to Translation Memories

Within the last 5 or so years the way translation memories are utilized go

beyond the familiar perfect-fuzzy match scheme of complete translation units.

Particularly interesting in this context are the so-called "subsegment

matching," predictive typing, and the combination of TM resources with

terminology and machine-translated data.

You can find some more information about the process in this blog post: xtm-

intl.com/blog/xtm-advanced-text-aligner-3.

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Subsegment Matching

Subsegment matching uses a principle pioneered by MultiTrans early on but

only now realized by a number of other tools (Trados Studio, memoQ,

Déjà Vu, CafeTran Espresso, Memsource) using various methods. The idea is

this: The chance that one particular segment (typically a sentence) will ever

be repeated is relatively slim, unless you are translating version 1.1 of a

manual for which you can use the translation memory that was created

translating version 1.0. Still, there is plenty of material in the translation

memories that can is useful. Since the very early days, tools offered

concordance searches, i.e., manual searches for a particular part of a segment

in a translation memory. While these searches were helpful, they were not

only tedious but completely based on the translator’s intuition of what might

or might not be in the TM. The idea of automated subsegment searches solves

that dilemma by automatically displaying all applicable subsegment matches

to the translator, and in the process essentially freeing up the 98% or so in

the TM that would otherwise be useless.

Figure 180: memoQ’s subsegment matching feature (LSC or "Longest Substring Concordance"

in memoQ lingo)

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Predictive Typing

The second relatively new feature is essentially an extension of the first. We

all are familiar with predictive typing in Excel or the address field of an

Internet browser, typically called AutoComplete. In those cases, a "database"

of previous entries into the Excel spreadsheet or the browser is consulted if

there are any matches to the first few keystrokes. If so, those matches are

displayed and offered to the user for entry with a single keystroke.

The same principle is used in the TEnTs that offer this feature (among them

Trados Studio, memoQ, Déjà Vu, Across, Star Transit and Wordfast Classic),

only that they in some way or the other use the content of the translation

memory to furnish these suggestions. Depending on your typing habits, this

can be a huge time saver, and it increases consistency significantly.

Figure 181: Wordfast Classic’s autocomplete feature

Combination of Linguistic Resources

An oddly under-used feature that involves the handling of translation memory

and other data is memoQ’s, CafeTran Espresso’s or Déjà Vu’s assemble

capability and the possibility of fuzzy match repair with the help of other TM

content, termbase entries, or even machine-translated data.

Computer-Assisted Translation Tools

In the following three screenshots, you can see three different levels of repair

of fuzzy matches that might give you a good idea of the power and versatility

of this feature. While this is obviously a project set up for demo purposes, it

still uses "real data." The only content in the translation memory are three

segments that have already been translated, and the glossary contains only

one term.

Here Déjà Vu X3 "repairs" a fuzzy match by utilizing data from the glossary

("lexicon"). Because the glossary contains the correct translation of the term

that is incorrect in the fuzzy match, it can automatically switch these and turn

the fuzzy match into a perfect match:

Figure 182: Déjà Vu X3’s repair feature using glossary data

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272

In the following screenshot, another fuzzy match is repaired even though the

glossary was of no help. Here Déjà Vu X3 subsegmentation

("DeepMining")was able to determine the correct translation because the term

in question appeared twice in the segments that are already in the translation

memory:

Figure 183: Déjà Vu X3’s repair feature using subsegment data

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