Dialogue generation method based on near-end strategy optimization and adversarial learning
A technology of reinforcement learning and optimization algorithms, applied in biological neural network models, special data processing applications, instruments, etc., can solve problems such as low utilization rate of rewards, reduced training efficiency, insufficient complexity, etc., to improve utilization rate and efficiency Effect
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 In order to have a clearer understanding of the model structure, purpose, and effects of the present invention, specific implementations of the present invention will now be described with reference to the accompanying drawings.
 figure 1 Is the method flow chart of the present invention:
 The first step: pre-training the generative model.
 The generative model uses an encoder-decoder architecture with an attention mechanism. Both the encoding part and the decoding part of the generative model are composed of cyclic neural networks. The encoding part encodes the input dialogue into a vector representation, and uses the attention mechanism to get the influence of each word in the input dialogue on the words that will be generated in the decoding process, and then generates the output conditionally.
 The purpose of the generative model is to maximize the probability that each output is a true answer:
 In formula (1), θ represents...
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