  Annotation through crowdsourcing draws incremental attention, which relies on
an effective selection scheme given a pool of workers. Existing methods propose
to select workers based on their performance on tasks with ground truth, while
two important points are missed. 1) The historical performances of workers in
other tasks. In real-world scenarios, workers need to solve a new task whose
correlation with previous tasks is not well-known before the training, which is
called cross-domain. 2) The dynamic worker performance as workers will learn
from the ground truth. In this paper, we consider both factors in designing an
allocation scheme named cross-domain-aware worker selection with training
approach. Our approach proposes two estimation modules to both statistically
analyze the cross-domain correlation and simulate the learning gain of workers
dynamically. A framework with a theoretical analysis of the worker elimination
process is given. To validate the effectiveness of our methods, we collect two
novel real-world datasets and generate synthetic datasets. The experiment
results show that our method outperforms the baselines on both real-world and
synthetic datasets.
