论文标题
使用生成对抗网络的用户定义主题的有效文本生成
Efficient text generation of user-defined topic using generative adversarial networks
论文作者
论文摘要
这项研究的重点是使用生成对抗网络（GAN）有效地生成文本。假设目标是生成一个用户定义的主题和情感趋势的段落，则通常必须对整个网络进行重新训练，以每次用户更改主题时每次获得新的结果。这将是耗时且不切实际的。因此，我们提出了一个用用户定义的GAN（UD-GAN），该GAN（UD-GAN）带有两级歧视器来解决此问题。第一个歧视者旨在指导生成器学习由多LSTM构建的段落级信息和句子句法结构。第二个应对更高级别的信息，例如用户定义的情感和文本生成主题。采用基于TF-IDF和长度惩罚的余弦相似性来确定该主题的相关性。然后，如果修改了文本生成的主题或情感，则第二个鉴别器将通过生成器重新训练。进行系统评估是为了将提出方法的性能与其他基于GAN的方法进行比较。目的结果表明，所提出的方法能够生成比其他时间少的文本，并且生成的文本与用户定义的主题和情感有关。我们将进一步研究将更详细的段落信息（例如语义）纳入文本生成以增强结果的可能性。
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.
