[VHRanger]

For a lot of cases, word2vec/glove still work plenty well. It also runs much faster and lighter when doing development -- the FSE library [1] does 0.5M sentences/sec on CPU, whereas the fastest sentence_transformers [2] do something like 20k sentences/sec on a V100 (!!).

For the drawbacks:

Word embeddings are only good at similarity search style queries - stuff like paraphrasing.

Negation they'll necessarily struggle with. Since word embeddings are generally summed or averaged into a sentence embedding, a negation won't shift the sentence vector space around the way it would in a LM embedding.

Also things like homonyms are issues, but this is massively overblown as a reason to use LM embeddings (at least for latin/germanic languages).

Most people use LM embeddings because they've been told it's the best thing by other people rather than benchmarking accuracy and performance for their usecase.

1. https://github.com/oborchers/Fast_Sentence_Embeddings

2. https://www.sbert.net/docs/sentence_transformer/pretrained_m...