Hello,
I read quite a lot about the importance of word embedding in the context of NLP, but i’ve never seen the following issue beeing adresed : Are pre-trained embeddings (word2vec, GloVe etc…) performing better or worse than an embedding layer trained along with the model ?
I intuitively would think that an embedding layer trained along with the model should perform better since it’s trained task-specifically by the network during back-propagation (and maybe generalize less easily ?) but didn’t really find much info on this.
I’m aware that there is probably no clear answer to this, and that this is definitely case dependant, but i’d emjoy hearing your feedback on this matter.
Cheers !