Unsupervised Document Embedding With CNNs
Table 1: Inference speed.
['Model', 'tokens / s']
After GLU layers we apply max pooling (for CNN-pool) and a single fully connected layer that outputs the 300-dimensional embedding. These results are generated by doing inference with batch size 1 to remove the effects of batch CPU/GPU parallelization. From the table it is our CNN architecture is over 10x faster than uni-directional skip-thought and over 20x faster than the bi-directional version. This is despite the fact that CNN has 7 hidden layers vs skip-thought RNN only has one.