Memória de tradução

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.

Creating 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:

1. Open your profile home page and select Resources > Translation Memory.
2. Click Create TM.
3. In the appeared dialog, name your TM and select a default language that will be displayed first in the table of TM records.
4. (Optional) Assign the TM to the needed projects. You can skip this step and assign a TM later.
5. Clique Criar.

For cases when you need to create a TM based on the translated Crowdin project, we recommend using the Translation Memory Generator app.

Managing TM Records

You can edit and delete existing TM records of a particular TM or all available TMs.

Editing TM Records

You can edit both a source and translation part of the existing TM record.

To edit a TM record, follow these steps:

1. Open your project and go to Resources > TM. Alternatively, open your profile home page and select Resources > Translation Memory.
2. Click All Records to view TM records of all available TMs in one list. Alternatively, select the needed TM and click View Records.
3. Open a TM record with a double-click or click Edit.
4. In the appeared dialog, edit or delete the records of the needed languages.
5. Click Save.

Deleting TM Records

You can delete one, multiple, or all the TM records at once.

To delete all the records from TM, follow these steps:

1. Select the top checkbox above the TM record list.
2. Confirm the selection of all TM records.
3. Click .

When dealing with the removal of TM records and translations, there could be three possible outcomes:

• When deleting a TM record from TM, the related translation won’t be deleted for a string in your Crowdin project.
• When you cancel the translation activity for a string via the Activity tab, the translation for a string will be deleted, but the related TM record will be preserved in TM.
• When deleting a translation for a string in the Editor, both the translation and the related TM record will be deleted.

For cases when you need to clean your TM from duplicates and outdated TM records, we recommend using the TM Cleaner app.

Downloading and Uploading TM

To download or upload TMs, follow these steps:

1. Open your project and go to Resources > TM. Alternatively, open your profile home page and select Resources > Translation Memory.
2. Click View Records on the needed TM.
3. Clique em Baixar ou Carregar.

The project owner and managers can download and upload TM in the following file formats: TMX, XLSX, or CSV.

If you upload TM in CSV or XLS/XLSX file formats, match columns with the corresponding languages in the configuration dialog.

Downloading TM for Offline Management

When downloading TM from Crowdin 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. 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. lastusagedate – The last date a translation suggestion was used in Crowdin.

Often translation vendors that work in Crowdin 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). 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.

Atribuindo TM

To assign a TM to your project, follow these steps:

1. Open your project and go to Settings > Translation Memory.
2. Selecione as MTs necessárias na lista.

Prioritizing TM

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:

1. Open your project and go to Settings > Translation Memory.
2. Set the preferred priority for assigned TMs from the respective drop-down list.

Changing Default TM

To change your project’s default TM, follow these steps:

1. Open your project and go to Settings > Translation Memory.
2. Click the star icon next to the needed TM from the list.

Sharing TMs

Using the shared TMs, you can pre-translate any of the projects you own. Also, TM suggestions from all TMs will appear in the Editor.

To share TMs between all of the projects you own, follow these steps:

1. Open your profile home page and select Resources > Translation Memory.
2. Select Share TMs.

Aplicando memória de tradução via pré-tradução

Pre-translation via TM allows you to leverage a minimum of 100% and Perfect matches. Read more about how TM matches are calculated.

1. Open your project and go to the Home tab.
2. Clique em Pre-traduzir.
3. Select via TM from the drop-down list.
4. Select files and languages you want to be pre-translated.
5. Configure additional settings for translations.
6. Clique em Pre-traduzir.
   Note: If the auto-substitution feature is enabled, TM suggestions improved to 100% will be treated as regular 100% matches.

   

You can set up the Pre-translation to be performed automatically when new files are uploaded. This feature is available as a part of Custom Workflow (only for the Team plan).

Prioritizing TM Suggestions during the Pre-translation via TM

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.

1. Relevance – also known as TM match. Read more about TM Match Calculation.
2. Auto-Substitution usage – verifying whether the TM suggestion was improved by the auto-substitution. Read more about Auto-substitution.
3. Assigned TM Priority – the priority of the TM a TM suggestion is stored in. Read more about Prioritizing TM.
4. Primary or dialect language – the primary or dialect language usage in TM suggestion’s source text (e.g., a TM suggestion from English will have a higher priority than English, Canada).
5. TM suggestion creation date – the date a TM suggestion was created (a TM suggestion with a more recent creation date will have a higher priority).

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.

• The system finds two TM suggestions with the source text Welcome and Welcome!. The translation from the Welcome! TM suggestion will be used since it has a higher TM match.
• The system finds two TM suggestions: Welcome! and 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.
• The system finds two TM suggestions: Welcome! and 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.
• The system finds two TM suggestions: Welcome! and 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.
• The system finds two TM suggestions: Welcome! and 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.

Cálculo de correspondência MT

Crowdin 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:

• Perfect Match - TM segment’s text and context completely matches the source string
• 100% Match - TM segment’s text matches the source string, but the context is different
• Fuzzy Match (99% and less) - TM segment’s text is different to a certain extent compared to the source string

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:

• Word order
• Punctuation
• Formatting tags
• Matches that are longer than the source string

Veja também

• Auto-substitution
• Custom Workflows