This is a funny question because of 'machine learning' (ML), 'still', and 'better'. Presumably you mean 'machine learning methods in NLP' (Natural Language Processing), because I'm having a hard time thinking of linguistic theory that informs ML uses outside of NLP. 'Still' implies it has a 'better' one now, and 'better' implies a current one is not sufficient. That is a bit too motivated in one very particular direction, so I will just answer simply just how linguistic theory and NLP implementation are involved together.
The latest popular and successful methods for things like [Seq2Seq] translation and chat (https://en.wikipedia.org/wiki/Neural_machine_translation) models, such as RNN or LSTM, use barely any linguistic knowledge at all (little more than "here's a sequence of characters that might be whitespace-separated 'words'"). No parsing/phrase structure grammars, POS (parts of speech), anaphora resolution. Some NLP methods may use these linguistic ideas but they are almost entirely avoided in methods that are ML based.
Historically, there was an attempt to use syntactic parsers, and those did well-enough, but the these latest statistical methods have been much more accurate.
In the narrower field of speech-to-text (a common stage before NLU), some phonological theory is used, but its mostly years of incremental engineering that have produced the high quality you have today.
There's a famous quip by Frederick Jelinek about speech processing:
"Every time I fire a linguist, the performance of the speech recognizer goes up"
This isn't saying that linguistic theory is necessarily useless, just that... maybe... those trained in language theory aren't as good at implementation as maybe others?
Anyway, I suspect that once a lot of accuracy has been squeezed out of tweaks of the Deep Learning methods mentioned above, there will have to be some use of explicit knowledge of language specifics to get things 'human'.
For example, current statistical machine translation has trouble with presumably simple things like getting gender right, which is hardly deep linguistic theory
Douglas Hofstadter has a recent Atlantic article about the shortcomings of purely statistical methods. His simple example about gender translation is:
There’s his car and her car, his towels and her towels, and his library and hers.
which currently translates to
Il y a sa voiture et sa voiture, ses serviettes et ses serviettes, sa bibliothèque et la sienne.
which only gets the French agreement right on the last pair.
In the other direction though is this article about how current methods are pushing up against the limits of what they can do without linguistics. At first it shows how little linguistics is actually used, and then goes on to claim that linguistic models may be added to translators soon.
This is all related to the rule based vs statistical controversy in AI.
