Neural Academic Paper Generation

In this work, we tackle the problem of structured text generation, specifically academic paper generation in LaTeX, inspired by the surprisingly good results of basic character-level language models. Our motivation is using more recent and advanced methods of language modeling on a more complex dataset of LaTeX source files to generate realistic academic papers. Our first contribution is preparing a dataset with LaTeX source files on recent open-source computer vision papers. Our second contribution is experimenting with recent methods of language modeling and text generation such as Transformer and Transformer-XL to generate consistent LaTeX code. We report cross-entropy and bits-per-character (BPC) results of the trained models, and we also discuss interesting points on some examples of the generated LaTeX code.