Markov models are best suited to situations where an observation k-steps in the past gives exponentially less information about the present[2] (decaying according to something like λ^k for 0 <= λ < 1). Intuitively, the amount of context imparted by a word or phrase decays somewhat more slowly. That is, if I know the previous five words, I can make a good prediction about the next one, and likely the next one, and slightly less likely the one after that, whereas in a Markovian setting my confidence in my predictions should decay much more quickly.

So in answer to the grandparent, such a thing should be reasonably straightforward to build if it doesn't exist already, and it may offer improvements over a similar model based on Markov chains.

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0. https://www.youtube.com/watch?v=5MdSE-N0bxs

1. https://arxiv.org/abs/1606.06737

2. Why is this? Lin & Tegmark offer details in the paper, but it comes from the fact that the singular values of the transition matrix are all less than or equal to one (an aperiodic & ergodic transition matrix has only one singular value equal to one), and so the other singular vectors fall away exponentially quickly, with the exponent's base being their corresponding singular value.