I'll start this post by clarifying the difference between AutoSuggest and AutoSuggest Dictionaries.
This is SDL Trados Studio's functionality that allows the user to receive suggestions from multiple sources after typing the first letter or letters of the intended translation.
AutoSuggest settings are accessed via Options - AutoSuggest. Here, one can decide which providers to enable by checking or unchecking the appropriate boxes. Further settings are available for the Translation Memory and Automated Translation provider, where one can decide whether fuzzy and concordance matches should be used, for example.
As we can see in the screenshot above, AutoSuggest Dictionaries are one of the AutoSuggest providers one can use.
AutoSuggest Dictionaries need to be created by selecting a translation memory and following the Create AutoSuggest Dictionary wizard found in the Translation Memories view. Once the creation process is started, Studio will automatically handle phrase extraction with no user intervention.
At the end of the process, a *.bpm file will have been created, which will then need to be added via Options/Project Settings-Language Pairs-Specific Language Pair to be available as an AutoSuggest provider.
There are two things worth noting here:
- The actual contents of the AutoSuggest Dictionary are somewhat of a mystery, as one cannot browse or edit the content in any way.
- In order to create an AutoSuggest Dictionary, Studio must have the appropriate functionality installed. However, even if a user can't create AutoSuggest Dictionaries, they can still use dictionaries shared by others.
The case for AutoSuggest Dictionaries
While the amazing technology behind fragment matches (available since Studio 2017) has made a great difference as recently entered translations are offered as AutoSuggest hits, I have found that fragment matches are not necessarily a substitute for well-curated AutoSuggest Dictionaries, as the suggestions from both sources tend to differ and in some cases AutoSuggest Dictionaries offer more relevant hits.
To illustrate this, I've collected several examples from an actual project I'm working on. The TM I'm using for this project (and where the fragment matches are coming from) is the same TM that was used to create the active AutoSuggest Dictionary. So I guess that would mean that if UpLift fragment matching really makes AutoSuggest Dictionaries redundant or obsolete, we would see the same suggestions coming from both, or, at the very least, the suggestions from the AutoSuggest Dictionary would not be as useful, right? Let's see.
Have a look at this example, where the source text contains the phrase "prescription medications", and the following AutoSuggest entries are offered.
Green icon = Termbase Green and yellow icon = Fragment matches Blue icon = AutoSuggest Dictionary
The backtranslation of the above is:
medications (I'm guessing, this one isn't in Spanish!)
As we can see, there are suggestions coming from the termbase (1) and fragment matches (4, although the last one is certainly not Spanish), but the most valuable suggestions are the first 3 coming from the AutoSuggest Dictionary.
Here's another instance from the same project, triggered by typing the first letter for the translation of the word "treatment", where the AutoSuggest window doesn't even include any fragment matches.
Without the AutoSuggest Dictionary, all those suggestions would be lost.
And here's yet another instance, where we can see that sometimes only the AutoSuggest Dictionary returns hits.
In the example below, I get both fragment matches and AutoSuggest Dictionary hits, but while the fragment match is close, the exact one I need "salir del hospital" (no punctuation) comes from the dictionary.
I could add many more examples, but I think this illustrates my point. While fragment matches via UpLift technology are an incredibly valuable resource during translation, they do not render AutoSuggest Dictionaries obsolete or useless, and we would be doing ourselves a disservice if we stop using them because of this misconception. We should definitely take advantage of both for a richer AutoSuggest experience.