Comments (6)
Hi @vekat !
Sorry for not replying earlier I was quite busy lately.
Thank you for using mlconjug3 and submitting this feature request.
I think it is a great suggestion and I had been pondering about the same issues for a while regarding how to handle defective verbs.
I will implement an optional feature to fill the gaps for defective verbs as you suggest, it is the most elegant way to deal with this issue.
I will start implementing this during the week and I will definitely keep you informed and maybe ask for your feedback on the implementation before releasing a new version.
Thanks again for your helpful feedback.
Cheers,
SekouDiaoNlp.
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Hi @vccortez .
Thank you again for submitting this issue.
I think I found a scheme that will work pretty robustly across different languages.
I am going to implement first some new languages before the summer ( at least Catalan, Valencian, Czech and Polish).
I will then implement your suggestion for defective verbs.
Cheers,
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Hi @vccortez, @sfc32 .
Sorry for the late update, but I unfortunately contracted covid and I had to stay put for a while.
However I am now back to full form and will soon update the project with the fill-in for defective verbs.
Cheers,
SekouDiaoNlp.
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Hi @vekat ,
It is correct that the defective verbs are already modeled as defective in the training data.
I want to keep it that way by default and add an optional parameter to fill-in defective verbs by the machine learning model.
I have a prototype ready that should be released by the end of the month/beginning of next month.
To make this fill-in feature robust and applicable in all languages I am currently implementing Optimality Theory because depending on the substitution done to the defective verb, the model will choose a different conjugation paradigm.
I will let you know as soon as the feature is implemented.
Have a nice day!
SekouDiaoNlp.
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Hi @vekat , how are you?
I am currently investigating how to best implement the custom handling of defective verbs.
Your idea of substituting the first letter/syllable of the infinitive is a good one, but after consulting with a phonologist colleague, it appears that it is not as simple as I might have assumed at first.
The substituted syllable must have similar phonological characteristics to the original one.
I am in the process of implementing a model of Optimality Theory into mlconjug3 to make the substitution robust in all supported languages.
It is going to take a bit of time to implement, but after that mlconjug3 will be able to conjugate defective verbs in all persons and tenses in a consistent manner.
I will let you know as soon as I have a working prototype.
Cheers,
SekouDiaoNlp.
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Hello @SekouDiaoNlp, I don't know exactly what optimality theory means in this context, but the idea of substituting letters that I wrote under "Describe alternatives you've considered" was not really how I expected this issue to be solved from your perspective as the library author. It was just me brainstorming how I could solve my specific issue by circumventing the API, so, from the point of view of a user.
What I really meant was that your library already knows how to conjugate verbs that don't exist, and this knowledge could be used (conditionally) to fill in the holes purposely left by defective verbs. If I understood the project correctly, the reason why defective verbs are missing conjugations is not because the neural net has learned what should be defective and what shouldn't, but because they are already modelled as defective (in the mlconjug3/data/conjug_manager/ data), correct?
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Related Issues (20)
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