  Crowdsourcing is regarded as one prospective solution for effective
supervised learning, aiming to build large-scale annotated training data by
crowd workers. Previous studies focus on reducing the influences from the
noises of the crowdsourced annotations for supervised models. We take a
different point in this work, regarding all crowdsourced annotations as
gold-standard with respect to the individual annotators. In this way, we find
that crowdsourcing could be highly similar to domain adaptation, and then the
recent advances of cross-domain methods can be almost directly applied to
crowdsourcing. Here we take named entity recognition (NER) as a study case,
suggesting an annotator-aware representation learning model that inspired by
the domain adaptation methods which attempt to capture effective domain-aware
features. We investigate both unsupervised and supervised crowdsourcing
learning, assuming that no or only small-scale expert annotations are
available. Experimental results on a benchmark crowdsourced NER dataset show
that our method is highly effective, leading to a new state-of-the-art
performance. In addition, under the supervised setting, we can achieve
impressive performance gains with only a very small scale of expert
annotations.
