Artificial Intelligence


Dynamic Cascade Neural Network

Authors: Tofara Moyo

We introduce a type of cascade correlation network to predict the next word in a sentence, where the hidden layer is connected in such a way as to represent the topology of the conversation up to that point. There will be one input neuron that is fed the current word, and one output neuron that will output the predicted next word in the sentence. During training, we will be given a set of conversations to train on. That means that we shall know the input output pairs to train with, and the topology of the previous parts of the conversation correlating with each pair. We shall build the hidden layer in such a way that it is isomorphic to the previous parts of the conversation at the point here that input output pair coincide. During operation we will build the hidden layer of the network at each input word and in such a way as to be isomorphic to the previous parts of the conversation up to that point. Training will make the probability of the output n+1 word to be conditionally dependent on the previous words in that order and not just the nth word, conditional on the training conversations giving the chat-bot the ability to talk consistently on topics and themes found within the training conversations.

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Submission history

[v1] 2019-04-24 08:50:57

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