Active learning aims to select a small subset of data for annotation such
that a classifier learned on the data is highly accurate. This is usually done
using heuristic selection methods, however the effectiveness of such methods is
limited and moreover, the performance of heuristics varies between datasets. To
address these shortcomings, we introduce a novel formulation by reframing the
active learning as a reinforcement learning problem and explicitly learning a
data selection policy, where the policy takes the role of the active learning
heuristic. Importantly, our method allows the selection policy learned using
simulation on one language to be transferred to other languages. We demonstrate
our method using cross-lingual named entity recognition, observing uniform
improvements over traditional active learning.

该论文提出了将主动学习重新表述为一种强化学习问题，并明确学习数据选择策略，其中策略充当主动学习启发式算法的角色，以解决启发式选择方法的有效性受限且性能因数据集而异的问题。我们使用跨语言命名实体识别来演示我们的方法，发现相对于传统的主动学习，我们的方法获得了均衡的改进。