Should LLM tech be used optimize the Gramps Weblate glossary

Development
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The Weblate glossary for Gramps has grown to over 13 thousand strings. It seems like that should be sufficient to communicate most concepts. Yet the glossary continues to grow. (When I recall checking the count last year, wasn’t it around 8 thousand?)

Should there be a new part of the source validation process that compares translatable strings to the existing strings? Where it finds new entries and recommends a possible equivalent string from the existing glossary?

Another LLM process might be to find phrases that are re-wordings of each other. Where it could recommend consolidations.

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When looking at the “components” breakdown, the string counts look more like what I had recalled:

  • Gramps 7.2k strings
  • Addons 5.2k strings
  • Web (GrampsWeb) 227 strings
  • Glossary 680 strings

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3

In theory this is a good idea as long as the processes that are used are able to understand the semantics of the strings in question.

Within Gramps core code de-duplicating strings makes, since it would promote consistency. In the addons repo it may not be easy since addon authors might want more autonomy to change strings without affecting all other addons.

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Semantic context is an interesting point. Does Weblate provide such context for translators?

Noticed some strings are duplicated with a numeration annotation. Does that mean that th string requires different translation sometimes?

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