Very Deep Convolutional Networks for Text Classification
Table 5: Testing error of our models on the 8 data sets. No data preprocessing or augmentation is used.
['Depth', 'Pooling', 'AG', 'Sogou', 'DBP.', 'Yelp P.', 'Yelp F.', 'Yah. A.', 'Amz. F.', 'Amz. P.']
S4SS3SSS0Px1 Our deep architecture works well on big data sets in particular, even for small depths. For the smallest depth we use (9 convolutional layers), we see that our model already performs better than Zhang’s convolutional baselines (which includes 6 convolutional layers and has a different architecture) on the biggest data sets : Yelp Full, Yahoo Answers and Amazon Full and Polarity. The most important decrease in classification error can be observed on the largest data set Amazon Full which has more than 3 Million training samples. We also observe that for a small depth, temporal max-pooling works best on all data sets.