Neural Academic Paper Generation
This addresses the challenge of structured text generation for researchers and developers, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of generating academic papers in LaTeX using advanced language models like Transformer and Transformer-XL, reporting cross-entropy and bits-per-character results on a dataset of computer vision papers.
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.