Efficient text generation of user-defined topic using generative adversarial networks

• URL: http://arxiv.org/abs/2006.12005v1
• Date: Mon, 22 Jun 2020 04:49:47 GMT
• Title: Efficient text generation of user-defined topic using generative adversarial networks
• Authors: Chenhan Yuan, Yi-chin Huang and Cheng-Hung Tsai
• Abstract summary: We propose a User-Defined GAN (UD-GAN) with two-level discriminators to solve this problem. The proposed method is capable of generating texts with less time than others.
• Score: 0.32228025627337864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study focused on efficient text generation using generative adversarial networks (GAN). Assuming that the goal is to generate a paragraph of a user-defined topic and sentimental tendency, conventionally the whole network has to be re-trained to obtain new results each time when a user changes the topic. This would be time-consuming and impractical. Therefore, we propose a User-Defined GAN (UD-GAN) with two-level discriminators to solve this problem. The first discriminator aims to guide the generator to learn paragraph-level information and sentence syntactic structure, which is constructed by multiple-LSTMs. The second one copes with higher-level information, such as the user-defined sentiment and topic for text generation. The cosine similarity based on TF-IDF and length penalty are adopted to determine the relevance of the topic. Then, the second discriminator is re-trained with the generator if the topic or sentiment for text generation is modified. The system evaluations are conducted to compare the performance of the proposed method with other GAN-based ones. The objective results showed that the proposed method is capable of generating texts with less time than others and the generated text is related to the user-defined topic and sentiment. We will further investigate the possibility of incorporating more detailed paragraph information such as semantics into text generation to enhance the result.

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