Sketching Word Vectors Through Hashing
Table 4: Comparison of our method against Glove and CBOW with respect to results obtained from the sentence-level classification task. Avg. denotes the arithmetic mean of the obtained f-scores across 4 independent runs. The column ‘Time’ shows the average training time in minutes.
['[EMPTY]', '[BOLD] CNN avg', '[BOLD] CNN Time', '[BOLD] RNN avg', '[BOLD] RNN Time']
Our method clearly outperforms GloVe in all the settings with a large margin. The poor performance of GloVe is particularly surprising given that it showed a relatively better performance than the other two methods in the intrinsic evaluations (specially the analogy task). Concerning the CBOW model, vectors built by our method could outperform the CBOW baseline when using the CNN model, provided that we use vectors of the larger dimenionality of 1000 (which consequently comes at the expense of a longer training time). Regarding the RNN model, the CBOW model outperforms our method particularly for vectors of low dimesnionality. As we increase the size of vectors obtained by our model, the performance gap between the two methods decreases.