This article goes over several strategies project managers can use to leverage Lilt Memories when optimizing localization workflows.
TMX file sizes
TM files can be uploaded directly to Lilt if they are 200 MB or smaller. For files larger than 200 MB, you can zip the TMX file and upload it with the extension
.tmx.zip. Note that TMX files are currently the only type of zipped memory file Lilt supports for import.
For a list of supported file types, see the Supported File Formats article.
TB CSV file format
Before uploading CSV files, you’ll want to check the Memory Settings for the following:
- If you want the entries you import to be immediately visible to translators, set the
Default TB Entry Statusto
- If you want the imported entries to be reviewed by a linguist before becoming available for use in the Lilt Editor Terminology Pane, set the
Default TB Entry Statusto
When uploading CSV files, the columns should be structured with the first two columns as source text and target text. If a row is missing either of these, the row will not be imported. Termbase CSV files can optionally include a header row as the first row.
CSV Termbase example:
source text,target text,status,created date,updated date
hello,hola,272542,2021-04-09 15:25,2021-04-09 15:25
where,dónde,272542,2021-04-10 16:33,2021-04-10 16:33
fly,volar,272542,2021-04-11 10:01,2021-04-11 10:01
listen,escuchar,272542,2021-04-12 13:48,2021-04-12 13:48
CSV Termbase example as viewed in a spreadsheet application:
Note: If en entry's source text or target text content contains commas, you will need to wrap it in quotes to let Lilt know where the text starts and ends. If you don't wrap the text in quotes, Lilt will assume the text ends at the first comma it finds.
CSV files with header rows can be imported by using the
Termbase (TB With Header) option when importing, as described in the Uploading Memory Files article.
Termbase CSV files can have as many metadata columns as you would like, and can also contain rows that omit any amount of metadata fields. Each metadata field needs to be separated by a comma. In the example screenshots above, the metadata columns are
created date, and
updated date. Metadata fields are useful for providing contents to translators.
Before uploading, make sure there are not any undesired spaces in the CSV file, as all spaces are included in the imported data, even spaces directly after commas.
TMX content clean-up
To save time from having to clean up TM entries in Lilt after uploading TMX files, it is best to clean up the files beforehand. If a Lilt Memory contains polluted TMX files, issues can arise with inconsistency and reduced productivity. Look out for the following when cleaning your TMX files:
- Sort through the older TM entries and remove entries that are outdated. This can be as easy as removing all entries that were last utilized before a given date.
- Remove duplicate TM entries that have the same source text but different target text.
TMX file naming convention
TMX files evolve over time, so to better keep track of your TMX files, it is helpful to name them with informative labels such as the date, type of content, and name of the product or project. Whatever format you choose, maintaining a consistent naming convention will help project managers stay organized by being able to easily identify which projects are associated with which Memories.
Example naming convention:
- [DATE]-[CONTENT TYPE]-[NAME]
In this example, [NAME] could be the product or the project. Examples using this naming convention:
TM repetition management
Every segment that gets translated in the Editor is added as a source/target TM entry within the associated Lilt Memory. If the same source/target pair is processed multiple times, this will result in duplicate TM entries in the Project's associated Lilt Memory. In the Editor’s Segment Context pane, duplicate entries are stacked to avoid confusion. In the example below, this stacking is displayed as
+1 identical result.
It is up to project managers to determine how often they want to remove duplicates, or whether they want to remove duplicates at all. On the
Manage Memory page of the
Memories tab, entries are sorted by the date they were last updated. If you want to find duplicates for a specific entry, use the search bar to filter the results.
Archiving projects to preserve TM entries
If you want to remove a project from the Projects List, you can either archive the project or delete the project.
Archiving a project will retain that project’s TM entries within the associated Lilt Memory. If you plan to reuse segments, archiving a project is generally a better option than deleting the project.
Deleting a project will also delete that project’s TM entries from within the associated Lilt Memory. If you want to delete a project, consider first downloading the Memory data using the
Export Memory As TMX option in case you want to utilize the project’s TM entries in the future by importing the data into a Lilt Memory.
Uploading TMX to Concordance only
When uploading TMX files, you have the option to upload them for Concordance only. This option makes it so the TMX files are indexed for Concordance but are not used to train the MT or as TM results. This can be a useful with outdated TMX files that still contain useful context and information you want to be made available to linguists under Concordance.