  When using active learning, smaller batch sizes are typically more efficient
from a learning efficiency perspective. However, in practice due to speed and
human annotator considerations, the use of larger batch sizes is necessary.
While past work has shown that larger batch sizes decrease learning efficiency
from a learning curve perspective, it remains an open question how batch size
impacts methods for stopping active learning. We find that large batch sizes
degrade the performance of a leading stopping method over and above the
degradation that results from reduced learning efficiency. We analyze this
degradation and find that it can be mitigated by changing the window size
parameter of how many past iterations of learning are taken into account when
making the stopping decision. We find that when using larger batch sizes,
stopping methods are more effective when smaller window sizes are used.
