Translation Memory (TM) is a database of the source strings and their corresponding translations into different languages that can speed up the translation of the same or similar strings in your projects. The project TM is created automatically for each project. Every translation made in the project is automatically added to the project TM.
Besides the project TMs that are automatically created along the respective projects, you can also create separate TMs, fill them with the appropriate content by uploading your existing TMs in TMX, XLSX, or CSV format, and then assign these TMs to the needed projects.
To create a TM, follow these steps:
You can create TM records from scratch, edit and delete existing TM records of a particular TM or all available TMs.
To create a TM record, follow these steps:
To edit a TM record, follow these steps:
You can delete one, multiple, or all the TM records at once.
To delete all the records from TM, follow these steps:
To download or upload TMs, follow these steps:
The owner, admins and managers can download and upload TM in the following file formats: TMX, XLSX, or CSV.
If you upload a TM in CSV or XLS/XLSX file formats, match columns with the corresponding languages in the configuration dialog.
When downloading a TM from Crowdin Enterprise in TMX format, you can get some additional metadata that might be useful for different usage scenarios with offline tools.
Additional TM attributes provided by translation memory downloaded in TMX format:
x-crowdin-metadata – String identifier hash.
creationid – Translation author’s full name and username in Crowdin Enterprise.
creationdate – Translation creation date.
changeid – Full name and username of the person who updated a translation.
changedate – Translation update date.
usagecount – Translation suggestion’s number of usages in Crowdin Enterprise.
lastusagedate – The last date a translation suggestion was used in Crowdin Enterprise.
Often translation vendors that work in Crowdin Enterprise export TMs from projects to manage them for their clients in various desktop applications (e.g., for cleaning TMs from irrelevant translations and further reimport back to Crowdin Enterprise). The TM attributes listed above allow better navigation and filtering of TM segments based on different criteria. Also, you might use cleaned and refreshed TMs to train MT engines only on product-specific data to ensure a higher quality of translations as a result.
To assign a TM to your project, follow these steps:
When you assign a few TMs to the project, you can set the needed priority for each of them. As a result, TM suggestions from the TM with the higher priority will be displayed in the first place.
The default TM priority value is set to 1. A higher number has a higher priority (for example, 5 has a higher priority than 1). For example, if you assigned four TMs to your project, you can set the priority of 4 to the most important TM, the one that should be used in the first place. And respectively set lower priorities to other TMs.
To set the priority for TMs, follow these steps:
To change your project’s default TM, follow these steps:
Using the shared TMs, you can pre-translate any of the projects in your organization. Also, TM suggestions from all TMs will appear in the Editor.
To share TMs between all of the projects in your organization, follow these steps:
Pre-translation via TM allows you to leverage a configurable (40% to 100% match ratio) and Perfect matches.
Read more about Pre-translation.
During the pre-translation via TM, the system considers multiple parameters to select the most relevant TM suggestion. If the system finds only one suitable TM suggestion for a string, it will be applied during the pre-translation via TM. If the system finds two or more TM suggestions for one string, they will be sorted based on multiple parameters and applies the most suitable one.
The following parameters are listed in the order the system uses them to decide which TM suggestion works better. If the decision can’t be made using the first parameter (i.e., two TM suggestions with 100% match), the system will use the next parameter until the decision is made.
To better understand how TM suggestions are prioritized during the pre-translation via TM, let’s go through a few hypothetical scenarios. Let’s imagine you have an untranslated string in your project with the following source text
Welcome!. Once you run the pre-translation via TM, the system starts to search for TM suggestions in your TMs.
Welcome!. The translation from the
Welcome!TM suggestion will be used since it has a higher TM match.
Welcome!. Both have the same source text, so the system checks whether the auto-substitution was used to improve these TM suggestions and picks the one that wasn’t improved by the auto-substitution.
Welcome!. Both have the same source text, and both weren’t improved by the auto-substitution. Then the system checks the priority of the TMs these TM suggestions are stored in and picks the one stored in the TM with higher priority.
Welcome!. Both have the same source text, both weren’t improved by the auto-substitution, and both are stored in the TMs with the same priority. Then the system checks the source languages of the TM suggestions and picks the one that uses the primary language.
Welcome!. Both have the same source text, both weren’t improved by the auto-substitution, both are stored in the TMs with the same priority, and both use primary source languages. Then the system checks the TM suggestion creation date and picks the one with the latest date.
In rarer cases, there could be a situation when two or more TM suggestions are identical based on all the parameters listed above. In this case, the system picks the first one among identical.
Crowdin Enterprise calculates the TM match by comparing the source string to be translated and TM’s existing segments.
There are three main types of TM matches:
If the Perfect and 100% TM match calculation is relatively straightforward, the fuzzy matches’ calculation may not be so obvious.
There are multiple different factors that affect the calculation of fuzzy matches, for example: