# Best Practices of Convolutional Neural Networks for Question Classification
## 1 Introduction
In [31], an extended CNN architecture is proposed, able to first classify a question into a broader category, and, successively, based on the prior knowledge, assign to it a more specific category. This solution was tested on an English questions dataset with pre-trained word embeddings, showing results on par or improved with respect to other classical methods. In [32], a simple and effective method for QC is presented, which increases generalization, by replacing entities with placeholders, and diversity of sentence features, by reading sentence vectors from both forward and reverse directions. This approach has shown better performance than many other complex CNN models, also proving its effectiveness applied to question answering systems. Finally, in [33], a QC approach based on word embedding using subword information and CNN is outlined, in order to improve classification accuracy. In particular, a comparison between English and Italian languages is reported, by highlighting eventual improvements obtained by initializing word embeddings with advanced vectors learned in an unsupervised manner and comprising character-based information. Summarizing, all the presented approaches based on CNNs for sentence classification, and specifically for QC, are characterized by models, whose structure is designed by hand by experts, thus requiring considerable skill and experience to select suitable hyperparameters such as the learning rate, the size of convolutional filters, the number of layers and so on. Moreover, these hyperparameters have internal dependencies, which make them particularly expensive for tuning and can depend on the specific classification task considered. Even though some recent works have shown that there exists much room to improve current optimization techniques for learning deep CNN architectures [34], fundamental working principles and behaviors of CNN models when specifically applied to QC have not been extensively investigated. The most relevant works addressing these issues are generally tested for text classification. In [21], different strategies for words representation are compared, by employing in turn, singularly or combined in a multi-channel way, differently initialized, and eventually fine-tuned WE vectors. On the basis of [21] model, a sensitivity analysis of CNNs is proposed in [19], summarizing the influences of various hyperparameters, i.e., WE vectors, filter size, number of filters, activation function, pooling strategy, and regularization. Both these works, for the QC task considered among the others, found different best settings with respect to the other tasks. However, to the best of our knowledge, related research considered only few settings, and without reference to possible interactions among them. Moreover, (i) QC was only considered as an instance of text classification; (ii) the possible relation with the language not taken into account. Thus, this work constitutes the first attempt of considering hyperparameters in a comprehensive way, examining different possibilities with respect to morphologically different languages, to study the problem of configuring the appropriate CNN architecture for QC.