翻译记忆

翻译记忆库（TM）是一个数据库，其中包含源字符串及其对应译文（翻译成不同的语言），它可以加快项目中相同或类似字符串的翻译速度。 每个项目都会自动创建一个项目翻译记忆库。 项目中进行的每一次翻译都会自动添加到该翻译记忆库中。

创建翻译记忆

除了自动创建的项目翻译记忆库之外，您还可以创建单独的翻译记忆库，通过上传现有 TMX、XLSX 或 CSV 格式的翻译记忆库来填充适当的内容，然后将这些翻译记忆库分配给需要的项目。

创建翻译记忆库的步骤如下：

1. Open your profile home page and select Resources > Translation Memories.
2. 点击 创建翻译记忆。
3. 在弹出的对话框中，命名您的翻译记忆库并选择默认语言，该语言将首先在翻译记忆库记录的表格中显示。
4. (Optional) Assign the TM to the needed projects. You can skip this step and assign a TM later.
5. 点击创建。

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

管理翻译记忆记录

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

编辑翻译记忆记录

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

编辑翻译记忆记录的步骤如下：

1. Open your profile home page and select Resources > Translation Memories. Alternatively, open your project and go to Settings > Translation Memories.
2. Select the needed TM and click View Records. Alternatively, click All Records to view TM records of all available TMs in one list.
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. 点击 保存。

In addition to editing TM records via the Translation memories page, read more about Editing TM Records in the Editor.

删除翻译记忆记录

您可以一次删除一个、多个或所有翻译记忆库记录。

要删除翻译记忆库中的所有记录，请遵循以下步骤：

1. 选择翻译记忆库记录列表顶部的全部复选框。
2. 确认选中所有翻译记忆库记录。
3. 点击 。

处理翻译记忆库记录和译文的删除时，可能会出现三种结果：

• When deleting a TM record from TM, the related translation won’t be deleted for a string in your Crowdin project.
• 当您通过“活动”选项卡取消字符串的翻译活动时，该字符串的译文将被删除，但相关的翻译记忆库记录将保留在翻译记忆库中。
• 在编辑器中删除字符串的译文时，译文和相关的翻译记忆库记录都将被删除。

如果您需要清理翻译记忆库中的重复项和过时记录，我们建议使用 TM Cleaner {:target=”_blank”}应用。

下载和上传翻译记忆

若要下载或上传翻译记忆，请遵循以下步骤：

1. Open your profile home page and select Resources > Translation Memories. Alternatively, open your project and go to Settings > Translation Memories.
2. Click View Records on the needed TM.
3. 点击下载或上传。

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

如果使用 CSV 或 XLS/XLSX 文件格式上传翻译记忆库，请在配置对话框中将对应语言的列匹配起来。

翻译记忆的 CSV 和 XLSX 文件格式的自动列识别

一旦您以 CSV 或 XLSX 格式上传翻译记忆库文件，系统会根据第一行指定的列名称自动检测文件架构。 The identification is performed in a case-insensitive manner. Columns that weren’t detected automatically will be left as Not chosen for manual configuration. 当您上传包含多种语言的翻译记忆库电子表格时，自动列识别会特别有用。

为了充分利用自动列识别功能，我们建议您使用以下值命名 CSV 或 XLSX 翻译记忆库文件中的语言列：

• 语言名称（例如：乌克兰语）
• Crowdin 语言代码（例如：uk）
• Locale (e.g., uk-UA)
• 带下划线的区域设置（例如：uk_UA）
• Language code ISO 639-1 (e.g., uk)
• Language code ISO 639-2/T (e.g., ukr)

要重新检测翻译记忆库文件架构，请单击检测配置。

Downloading TM for Offline Management

When downloading a 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.

分配翻译记忆

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

1. Open your project and go to Settings > Translation Memories.
2. In the Assigned Translation Memories section, select the needed TMs from the list.

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 Memories.
2. In the Assigned Translation Memories section, 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 Memories.
2. In the Assigned Translation Memories section, 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 Memories.
2. Select Share TMs.

通过预翻译应用翻译记忆

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

1. 打开您的项目并前往 仪表板 选项卡。
2. 点击 预翻译。
3. 从下拉列表中选择 通过翻译记忆 选项。
4. Select files and languages you want to be pre-translated.
5. Configure additional settings for translations.
6. 点击 预翻译。
   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 can be enabled and configured in the project Settings > Translation Memories.

Read more about Automated Pre-translation via TM.

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.

TM Match Calculation

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 match 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 calculations for Perfect and 100% TM match 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

另请参阅

• Auto-substitution