Post by habiba123820 on Nov 5, 2024 1:48:02 GMT -7
CAT tools, when properly configured, can more than double translation productivity and improve translation quality. The key here is how a CAT tool is configured. We need to look at: file analysis, segmentation, translation memories , glossaries, machine translation , and dozens of other important factors. KEY TAKEAWAY : A CAT tool is like a saw. Give the saw to someone who doesn’t know woodworking and you won’t see beautiful furniture. They’re more likely to injure themselves. So let’s look at some of the main issues related to CAT tools .
Problem 1: Poor analysis/segmentation
When you feed a file to a CAT tool, it processes that file, chewing up the text and stripping away all the coding so that the translator has something “clean” to work with. XML, XLIFF, DOCX, YAML. Regardless of the file format, the general process is the same. The challenge is that some files are written in ways that create confusing results for translators that can become impossible to deal with. Formatting can become ubiquitous tags that require careful handling, variables and code can appear wordpress web design agency as text, line breaks can incorrectly report sentence breaks and give translators an untenable situation to deal with. This happens more often than people realize in localization and is the first myth buster. A CAT tool won’t fix everything. In fact, it will open the hood and introduce even more complex problems into your localization workflow, despite the potential for much greater productivity. Without proper location engineering, the CAT tool can exacerbate segmentation and analysis problems that would otherwise be negligible outside the CAT tool environment.
Problem 2: Translation memory setup
Clarity about how you set up your knowledge base will be a determining factor in whether or not your experience with the CAT tool is successful . When it comes to translation memory , in my opinion, less is more. I often see clients and translators trying to squeeze as much as possible out of translation memories by stacking multiple translation memories together and trying to maximize the amount of content that is leveraged during the translation process. The challenge with this is that often users are not sure of the quality of a given translation memory. Sometimes they know that the quality is questionable and will apply penalties to that translation memory. A penalty will downgrade a match by a certain amount so that otherwise 100% matches will be reviewed and fuzzy matches will be downgraded to a lower range. While this is good in principle, in practice it creates an error-prone process for translators. Translation memories are meant to establish true north when it comes to the language corpus. If the translator naturally uses a certain word choice, but the TM uses another language to describe the same concept, the TM should always prevail. Working with shaky TMs introduces doubt and confusion into the translation process. Yes, in theory you can get more out of it and save more time and money with a larger translation memory, but we have seen time and time again that translation memories are an all-or-nothing deal. They either provide crystal-clear references or they detract from the overall quality of the translation process.
Problem 3: Multiple linguists working together
Many CAT tools do not focus on collaboration between different translators working on a given set of files at the same time. With translators working in a local environment via exported localization kits, translators are working in the dark when it comes to linguistic choices made by colleagues. This can lead to inconsistencies and poor knowledge sharing that ultimately burdens the review process with the task of standardizing translations. Reviewing maximizes quality when it comes to rereading, flagging, and correcting errors. As the scope of rewriting increases, the chances of introducing new errors instead of catching them increase. CAT tools that share translation memory feeds at runtime with translators in different locations go a long way toward optimizing knowledge management practices and overall quality at scale.
Problem 1: Poor analysis/segmentation
When you feed a file to a CAT tool, it processes that file, chewing up the text and stripping away all the coding so that the translator has something “clean” to work with. XML, XLIFF, DOCX, YAML. Regardless of the file format, the general process is the same. The challenge is that some files are written in ways that create confusing results for translators that can become impossible to deal with. Formatting can become ubiquitous tags that require careful handling, variables and code can appear wordpress web design agency as text, line breaks can incorrectly report sentence breaks and give translators an untenable situation to deal with. This happens more often than people realize in localization and is the first myth buster. A CAT tool won’t fix everything. In fact, it will open the hood and introduce even more complex problems into your localization workflow, despite the potential for much greater productivity. Without proper location engineering, the CAT tool can exacerbate segmentation and analysis problems that would otherwise be negligible outside the CAT tool environment.
Problem 2: Translation memory setup
Clarity about how you set up your knowledge base will be a determining factor in whether or not your experience with the CAT tool is successful . When it comes to translation memory , in my opinion, less is more. I often see clients and translators trying to squeeze as much as possible out of translation memories by stacking multiple translation memories together and trying to maximize the amount of content that is leveraged during the translation process. The challenge with this is that often users are not sure of the quality of a given translation memory. Sometimes they know that the quality is questionable and will apply penalties to that translation memory. A penalty will downgrade a match by a certain amount so that otherwise 100% matches will be reviewed and fuzzy matches will be downgraded to a lower range. While this is good in principle, in practice it creates an error-prone process for translators. Translation memories are meant to establish true north when it comes to the language corpus. If the translator naturally uses a certain word choice, but the TM uses another language to describe the same concept, the TM should always prevail. Working with shaky TMs introduces doubt and confusion into the translation process. Yes, in theory you can get more out of it and save more time and money with a larger translation memory, but we have seen time and time again that translation memories are an all-or-nothing deal. They either provide crystal-clear references or they detract from the overall quality of the translation process.
Problem 3: Multiple linguists working together
Many CAT tools do not focus on collaboration between different translators working on a given set of files at the same time. With translators working in a local environment via exported localization kits, translators are working in the dark when it comes to linguistic choices made by colleagues. This can lead to inconsistencies and poor knowledge sharing that ultimately burdens the review process with the task of standardizing translations. Reviewing maximizes quality when it comes to rereading, flagging, and correcting errors. As the scope of rewriting increases, the chances of introducing new errors instead of catching them increase. CAT tools that share translation memory feeds at runtime with translators in different locations go a long way toward optimizing knowledge management practices and overall quality at scale.