Learning how to Active Learn: A Deep Reinforcement Learning Approach
Table 2: Results from active learning using the different methods, where each approach constructs a training set of 200 sentences. The three target languages are shown as columns, reporting in each F1 score (%) and the relative cost reduction to match the stated performance of the Random strategy.
['[EMPTY]', '[BOLD] de F1', '[BOLD] de C/R', '[BOLD] nl F1', '[BOLD] nl C/R', '[BOLD] es F1', '[BOLD] es C/R']
It is clear that the Pal methods all outperform the baselines, and among these the multilingual training of Palm outperforms the bilingual setting in Palb. Surprisingly, Palc gives the overall best results, despite using a static policy and model during target application, underscoring the importance of model pretraining.