For the Active Learning loop, we set the number of batches L to 5. We tried different query batch sizes T = {10, 20, 30} and different data acquisition criteria (described in subsection 2.4.1) in separate trials. For each trial experiment, we chose to perform multiple iterations of batch AL instead of one, because of the advantages this may provide in terms of user experience and training efficiency. In fact, asking an expert to evaluate smaller batches iteratively instead of a larger batch at once may reduce the burden and prevent potential errors associated with fatigue and boredom from repetitiveness. Furthermore, iterative retraining on smaller batches can streamline the feedback process, since the expert can work with a model that becomes increasingly efficient at selecting the most informative or challenging instances in next AL iterations. Also, in cases where manipulation errors may occur (e.g., skipping a query or submitting an unintended answer), iterative batch active learning can help recover those. We set the number of rounds R to 4 in all trials.