Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification
Table 3: Summary of the network parameters used for experimental results. L2 is the default architecture used by our approach. The alternative L1 and L3 architectures are discussed in Figure 8.
['[EMPTY]', 'Number of layers', 'Number of filters', 'Filter window size  [ITALIC] h', 'Size of max-pooling window  [ITALIC] w and striding  [ITALIC] st']
on the English corpus as a function of the number of network layers. Each CNN was trained for one distant epoch. Comparing Network Architectures. One common question asked by practitioners relates to the influence of the number of layers on the performance of a neural network. We thus evaluated the performance of various architectures with multiple layers. In order to reduce the number of experiments, we evaluated the performance of the single-language model SL-CNN on the English set and varied the number of convolutional/pooling layer pairs from 1 to 3. We evaluated a total of 12 networks. The network performance generally improves with the number of layers, if a sufficient amount of training data is used in the distant-supervised phase.