<p>The closest think I've seen to what your are describing is attention mechanism in auto encoder. Where a Dense layer essentially control which of the encoded hidden states should be used by the decoding layer, instead of relying solely on the last hidden state.</p>
<p>here is the <a href="https://arxiv.org/pdf/1409.0473.pdf" rel="nofollow noreferrer">paper</a> if you want to read more.</p>
<p>This architecture intent at circumventing the limit on how much information can be stored in one hidden state over long sequence.</p>
RNN (LSTM) that uses up to the nth time step
<p>Usually in a RNN only the previous input and hidden state is used to calculate the output.
However, what would happen if we use up to n previous steps? In essence feeding an n-gram to the neural network?
Since n-grams are generally quite good in short text generation, this added information will lessen the burden in the hidden state to memorize short term knowledge and focus on the context aspect of the text.</p>
<p>This seems quite a simple thing but I'm unable to find any paper that have implemented this.</p>