Added Objective Guided Optimization of Adversarial Text Generation Method
Abstract
The problem of error accumulation is caused by the supervision of deep neural network text generation model. In
order to solve this problem, a text generation model based on the reinforcement of antagonistic thought training is proposed.The
adversarial network can be generated by the proposed model, and then the adversarial network can be used for identification,
the learning reward function can be optimized, and the generated model can be optimized to reduce the probability of error
accumulation.More text structure knowledge can be added into the generated text model by integrating the target guidance feature
into the actual generation process to make the generated text model have higher authenticity. In this paper, the author optimizes
the adversarial text generation method on the basis of target-guided optimization, which can be used for reference by practitioners.
order to solve this problem, a text generation model based on the reinforcement of antagonistic thought training is proposed.The
adversarial network can be generated by the proposed model, and then the adversarial network can be used for identification,
the learning reward function can be optimized, and the generated model can be optimized to reduce the probability of error
accumulation.More text structure knowledge can be added into the generated text model by integrating the target guidance feature
into the actual generation process to make the generated text model have higher authenticity. In this paper, the author optimizes
the adversarial text generation method on the basis of target-guided optimization, which can be used for reference by practitioners.
Keywords
Generative Adversarial Networks (GAN); Objective Guided Optimization; Antagonistic Text; Generate
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DOI: http://dx.doi.org/10.18686/ahe.v6i9.4776
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