> This seems like a very important paper, basically showing that Markov models with exponential decay of influence of tokens by distance are often a poor model, where as deep neural networks with LSTM (long short term memory) has power law decay of influence decay, which performs better for a variety of sequential data.

As someone outside of this field, it seems to me that this kind of result should have been very obviously foreseeable, hindsight bias and all that of course - but I would never have considered Markov processes to be an adequate predictability model for natural language. Though obviously the formalized results are important.

Could someone with more knowledge comment on what the current working assumptions were prior to this paper and what the consequences would be?