Abstract
In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.
Abstract (translated)
在过去几年中，由于生成对抗网络（GAN）的制定，在生成模型中已经取得了各种进步。已经证明GAN在与图像生成和样式转移有关的各种任务上表现得非常好。在自然语言处理领域，诸如word2vec和GLoVe之类的单词嵌入是用于在文本数据上应用神经网络模型的最先进方法。已经尝试利用具有字嵌入的GAN来生成文本。这项工作提出了一种使用Skip-Thought句子嵌入与基于梯度罚函数和f-度量的GAN相结合的文本生成方法。使用具有GAN的句子嵌入来生成以输入信息为条件的文本的结果与使用单词嵌入的方法相当。
URL
https://arxiv.org/abs/1808.08703
