Standard NN Problems:


1- Inputs, outputs can be different length in different examples.



2- Doesn't share features learned across different positions of a text.

University of Michigan - Ann Arbor

Google

The task of predicting what word/letter comes next, unlike the FNN and CNN, in sequence modeling the current output is dependent on the previous a=input and the length of the input is not fixed.


Why sequence models? 

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

A standard network assumes that each input is unrelated to other inputs. You want to use sequence models (like in sentences) when it is useful to know the prior inputs (or words). In addition, neural networks are useful when the lengths of inputs and outputs are constant. The main point is if the output is dependent on the previous input → use a sequence model.


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