When to Use Which Neural Network? Finding the Right Neural Network Architecture for a Research Problem

Considering the increasing rate of scientific papers published in recent years, for researchers throughout all disciplines it has become a challenge to keep track of which latest scientific methods are suitable for which applications. In particular, an unmanageable amount of neural network architectures has been published. In this paper, we propose the task of recommending neural network architectures based on textual problem descriptions. We frame the recommendation as a text classification task and develop appropriate text classification models for this task. In experiments based on three data sets, we find that an SVM classifier outperforms a more complex model based on BERT. Overall, we give evidence that neural network architecture recommendation is a nontrivial but gainful research topic.