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Neural Academic Paper Generation

2019-12-02 18:45:23
Samet Demir, Uras Mutlu, Özgur Özdemir
arXiv_CL
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/1912.01982

PDF

https://arxiv.org/pdf/1912.01982.pdf

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