[screye]

I makes sense to completely disregard language when looking at modern NLP solutions. In some sense, 'hand engineering' anything is looked down upon.

Transformers and scaling laws have made it such that the only thing that truly matters is your ability to build a model that can computationally and parametrically scale. The 2nd would be to figure out how to make more data 'viable' for usable within such a hungry model's encoding.

Look at anyone who has written the last 20 seminal papers in NLP, and almost none of them have a strong background in linguistics. Vision went through a similar period of forced obsolescence, during the 2012-2016 Alexnet -> VGG -> Inception -> Resnet transition.

It is unfortunate. But, time is limited and most researchers can only spare enough time to learn a few new things. Unfortunately for linguistics, it does not rank that high.