Text Feature Adversarial Learning for Text Generation With Knowledge Transfer From GPT2.

Text generation is a key component of many natural language tasks. Motivated by the success of generative adversarial networks (GANs) for image generation, many text-specific GANs have been proposed. However, due to the discrete nature of text, these text GANs often use reinforcement learning (RL) or continuous relaxations to calculate gradients during learning, leading to high-variance or biased estimation. Furthermore, the existing text GANs often suffer from mode collapse (i.e., they have limited generative diversity). To tackle these problems, we propose a new text GAN model named text feature GAN (TFGAN), where adversarial learning is performed in a continuous text feature space. In the adversarial game, GPT2 provides the "true" features, while the generator of TFGAN learns from them. TFGAN is trained by maximum likelihood estimation on text space and adversarial learning on text feature space, effectively combining them into a single objective, while alleviating mode collapse. TFGAN achieves appealing performance in text generation tasks, and it can also be used as a flexible framework for learning text representations.

Identifying Topics and Evolutionary Trends of Literature on Brain Metastases Using Latent Dirichlet Allocation.

Research on brain metastases kept innovating. We aimed to illustrate what topics the research focused on and how it varied in different periods of all the studies on brain metastases with topic modelling. We used the latent Dirichlet allocation model to analyse the titles and abstracts of 50,176 articles on brain metastases retrieved from Web of Science, Embase and MEDLINE. We further stratified the articles to find out the topic trends of different periods. Our study identified that a rising number of studies on brain metastases were published in recent decades at a higher rate than all cancer articles. Overall, the major themes focused on treatment and histopathology. Radiotherapy took over the first and third places in the top 20 topics. Since the 2010's, increasing attention concerned about gene mutations. Targeted therapy was a popular topic of brain metastases research after 2020.