Text generation method based on generative adversarial network

A network and text technology, applied in the field of text generation based on generative confrontation network, can solve the problems of limiting the potential of natural language processing and difficult training of GAN, and achieve the effect of enriching semantic and context information, increasing convergence speed, and improving structure

Inactive Publication Date: 2021-03-26
TONGJI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The proposal of SeqGAN makes the combination of reinforcement learning and GAN applied to text generation become the focus,

Method used

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  • Text generation method based on generative adversarial network
  • Text generation method based on generative adversarial network
  • Text generation method based on generative adversarial network

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Embodiment Construction

[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer, the text generation method according to the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention, not to limit the present invention, that is, the protection scope of the present invention is not limited to the following embodiments. Appropriate changes can be made by a skilled person, and these changes may fall within the scope of the invention as defined in the claims.

[0041] like figure 1 As shown in the block diagram of the structure, according to a specific embodiment of the present invention, the following steps are included:

[0042] 1) Preprocess the short text dataset and represent it as short text vector data mainly based on word embeddings. The MS COCO short text datas...

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Abstract

The invention discloses a text generation method based on a generative adversarial network, and relates to the field of text generation, in particular to a text generation method based on the generative adversarial network. According to the technical scheme, the truth value capable of guiding the network is obtained by utilizing the text data in the real data set, so that the purpose of increasingthe convergence rate of the generated network is achieved. According to the method, the distance between a generated text and a real text is measured by cosine distance and the like, and the distanceis added to a target function of a generated network, so that the target function is gradually optimized in the training process. Besides, a self-attention mechanism is added to the input layer to improve the structure of the discrimination network, so that the network can obtain richer semantic and contextual information, and the performance of the discrimination network is optimized. Accordingto the invention, the text data conforming to logic can be generated more stably.

Description

technical field [0001] The invention relates to the field of text generation, in particular to a text generation method based on a generative confrontation network. Background technique [0002] As an important research direction in the field of natural language processing, text generation technology has great application prospects. For example, it can be applied to intelligent question answering and dialogue, machine translation and other systems to achieve more intelligent and natural human-computer interaction; it can also replace editors with text generation systems to achieve automatic news writing and publishing. [0003] Text generation refers to the use of "machine learning + natural language processing" technology to enable computers to have human-level language expression capabilities, including machine translation, sentence generation, dialogue generation, etc. Usually, the strategy of text generation is with the help of language model, which is a probability-bas...

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Application Information

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IPC IPC(8): G06F40/211G06F40/30G06K9/62G06N3/04G06N3/08
CPCG06F40/211G06F40/30G06N3/049G06N3/08G06N3/045G06F18/24
Inventor 王俊丽吴雨茜韩冲张超波
Owner TONGJI UNIV
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