Unsupervised Document Embedding With CNNs
Table 2: IMDB sentiment classification test set accuracy. Test reviews are excluded during unsupervised model training.
['Method', 'Accuracy']
From the table, we see that our best model CNN-pool-k outperforms all baselines and passes the difficult 90% accuracy level. We also see that CNN-pool generally performs better than CNN-pad, suggesting that max pooling is more effective than padding and truncation. Both CNN-pool models significantly outperform the RNN-based skip-thought approach. These results indicate that convolution with max pooling is a good alternative to recurrence for unsupervised learning with variable length textual input.