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

TL;DR

This paper introduces UD-GAN, a novel generative adversarial network that efficiently generates user-defined topic and sentiment-specific text without re-training the entire model for each change, improving speed and relevance.

Contribution

The paper proposes a two-level discriminator GAN architecture that allows quick adaptation to new topics and sentiments in text generation without full re-training.

Findings

Faster text generation compared to existing GAN methods.

Generated texts are relevant to user-defined topics and sentiments.

System outperforms other GAN-based approaches in efficiency.

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.