I understand that discriminative models, such as CRF(Conditional Random Fields), model conditional probabilities $P(y|x)$, while generative models, such as HMM(Hidden Markov Model), model joint probabilities $P(y,x)$.
Take CRF and HMM for example. I know that CRF can have a larger range of possible features. Apart from that, what else makes CRF (discriminative models) preferable to HMM(generative models) in sequence labeling tasks such as Part-of-Speech tagging and NER(Name Entity Recognition)?
I found out that HMMs will have to model $P(x)$, while CRFs don't. Why would it make a big difference in sequence labeling tasks?