# Active Learning and RNN

We have a series of data which want to label part of each series. As we do not have any training data, we try using Active Learning as a solution. But, the problem is our classifier is something like RNN which needs a lot of data to be trained. Hence, we have a problem in converging fast to just label proportional small parts of unlabeled data. The question is "is there any article about this problem (Active Learning and some complex classifiers like RNN) or not?".

Or is there any alternative to approach this problem or not? (as data is a series of actions)

As I found this case backs to the sequence labeling. Sequence labeling has some classic solution such as conditional random fields (CRFs) and hidden Markov model (HMM). Also, have some solution in Active learning (AL) which use from algorihtms such as struct SVM ($$\text{SVM}^{\text{strcut}}$$) like this paper.
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