Single Status Update
I have a new idea, where Markov chains build sentences, for which the connections are chosen by neural networks. The max number of normalizable connections for each Markov node is 128. The problem here is how to find out how to form the reply, where the input is the sentence that came *before*. To retrieve it, I need a neural network that gets the next node from the current reply node, AND the input sequence. Seq2seq networks are not an option.
My possible solution would be to convert a phrase into a number using of an encoder neural network, e.g. with the phrase "Hello, my dear son!" we iterate from inputs [''Hello", 0] → A, where A is the output of the neural network. Then we do it again, but with the word "my", so that ["my", A] → A + B. And so on, until we convert that phrase to A + B + C + D — where the plus sign isn't a sum, but some sort of joining, that goes on inside the neural network.
That number is then passed into a decoder neural network, such that [0, A + B + C + D] → [N₁, A + B + C + D], and [N₁, A + B + C + D] → [N₂, A + B + C + D], ..., [0, A + B + C + D]. Nₙ is denormalized into the word that corresponds to the nth node that follows the node Nₙ₋₁
What about you? Any better solutions or suggestions? :)