A Practitioners’ Guide to Transfer Learning for Text Classification using Convolutional Neural Networks
Table 8: A comparison of accuracy and number of parameters against other methods
['Method', 'MR', 'SST-2', 'SST-5', '#Parameters']
Having presented the semantic relatedness and effect of various hyperparameters on the transfer performance, we now compare our results with some of the near state-of-the-art models. As can be seen, even with a small size, our CNN model under E\faUnlock C\faUnlock H\faUnlock O\faAsterisk setting outperforms state-of-the-art results on the MR dataset. For SST-2 it shows comparable performance even though the size is less than half of the other methods (DSCNN-P and TE-LSTM+c). Though the results for SST-5 are poor, there is still an improvement in the accuracy after transferring the knowledge. For a similar reason, the weights and the bias values of the O-layer are also not included.