Open macchiati opened 8 months ago
Principal categories that can be affected by Gender:
I'd also suggest to limit our scope to human gender (so only "you", "he/she", "I" will be taken into account, not "it").
S meitli isch ine cho, es het de öpfel gseh.
On Wed, Mar 13, 2024, 02:16 BrunoCartoni @.***> wrote:
Principal categories that can be affected by Gender:
- nouns
- pronouns
- adjectives
- verbs
I'd also suggest to limit our scope to human gender (so only "you", "he/she", "I" will be taken into account, not "it").
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This example is debatable (both "si" and "es" are possible).
As always, we should prioritize according to use case (e‧g.: do we need subject pronoun reference? Or is it more object pronoun?).
I jotted that down quickly on my phone (I agree that it isn't required to say 'es', though some people do. Just an example.)
I'm in general agreement with what you are saying. For the pronouns, I don't think we need 1st person at all; for 2nd person and 3rd person (and explicit people), we just need to know which particular categories are relevant for which language, for the localization tooling. The particular categories will just depend on the language, and we can have (eg) he/she/other if needed for a particular language.
On Wed, Mar 13, 2024 at 7:29 AM BrunoCartoni @.***> wrote:
This example is debatable (both "si" and "es" are possible).
As always, we should prioritize according to use case (e‧g.: do we need subject pronoun reference? Or is it more object pronoun?).
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This topic around the choice of words and associated human gender seems related to https://github.com/unicode-org/inflection/issues/7.
Here is a draft proposal for information that we could use in the very short term with MF2, to improve grammar of messages. While we would target MF2, the information could be used more broadly.
A. Enhance grammatical feature information
We could use more gender / noun-class information in order to switch among appropriate variant messages. To do this well, we need to know what the grammatical categories are. The localization tooling can then expand or contract the variant messages to be appropriate for the locale, much as it does for plurals right now in MF1.
Sample message
$user-gender
$person-noun-class
$object-noun-class
B. Test data for gender detection
We could prepare test data for at least one locale where we can derive the gender of people or objects, and use them in messages with a new function :noun-class
Sample message
C. Test data for case inflections
We could prepare test data for at least one locale where we can support case as an option:
Sample message
(We do have case data for units in CLDR, but it would be better if we had some more general examples.)