At each iteration, we train the model on the current train set and use a model-dependent query strategy to acquire new samples from the pool, get them labeled by an oracle and add them to the train set.
our findings are as follows: (i) We find that utilizing the uncertainty query strategy using a deep model like FastText.zip (FTZ) 1 to actively construct a representative sample provides query and train sets with remarkably good sampling properties.