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> 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,
by forgotpwtomain 10y ago
> 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?
- thomasahle 10y ago> I would never have considered Markov processes to be an adequate predictability model for natural language. Would you consider LSTM an adequate model?
- TheOtherHobbes 10y agoPersonally, no. I think all of these models are essentially trivial and a long way from genuine NLP. That doesn't mean they're not useful in very narrow domains. But language is pretty much the definition of the ultimate wide domain, and trying to cover it with statistical correlations makes as much sense as word counting Shakespeare to try to generate some new plays.
- mark_l_watson 10y agoI think you might be pleasantly surprised by recent results using DL and LSTM for building models of natural language. The next advancement I would like to see is handling anaphora resolution (resolving pronouns to previous noun phrases in text, resolving words like 'there' to a place mentioned elsewhere in text, etc.) Progress has been so rapid that I bet I don't have to wait long.
- nschucher 10y agoHave you seen the results from Dynamic Memory Networks? [0] The relevant example from the paper: I: Jane went to the hallway. I: Mary walked to the bathroom. I: Sandra went to the garden. I: Daniel went back to the garden. I: Sandra took the milk there. Q: Where is the milk? A: garden Obviously just a toy task, but as you said, progress is rapid! [0]: http://arxiv.org/abs/1506.07285 http://arxiv.org/abs/1506.07285
- mark_l_watson 10y agoThanks for the link!
- crypto5 10y ago> I think you might be pleasantly surprised by recent results using DL and LSTM for building models of natural language. What exactly those models are modelling?
- forgotpwtomain 10y ago> Deep models are important because without the extra “dimension” of depth/abstraction, there is no way to construct “shortcuts” between random variables that are separated by large amounts of time with short-range interactions; 1D models will be doomed to exponential decay. From my non-professional perspective the above seems that it should have been very obvious - (and also that correlations between variables for natural languages would be better explained by multi-dimensional structure). That is if you told me that this were proved / formally supported as it is in this paper, my reaction would be a "that sounds like reasonable approximation" not - "that result sounds very surprising I must read the paper".
- jakub_h 10y ago> but I would never have considered Markov processes to be an adequate predictability model for natural language Isn't it the case that for very short distances (several elements), power decay and exponential decay are (or can be made, with proper constants) more similar? Thus, if predictive models were originally studied only for very short sequences in the past (limited computational resources!), it seems to make sense that this is a mistake that anyone could have made more easily back then.