  Recent works of opinion expression identification (OEI) rely heavily on the
quality and scale of the manually-constructed training corpus, which could be
extremely difficult to satisfy. Crowdsourcing is one practical solution for
this problem, aiming to create a large-scale but quality-unguaranteed corpus.
In this work, we investigate Chinese OEI with extremely-noisy crowdsourcing
annotations, constructing a dataset at a very low cost. Following zhang et al.
(2021), we train the annotator-adapter model by regarding all annotations as
gold-standard in terms of crowd annotators, and test the model by using a
synthetic expert, which is a mixture of all annotators. As this
annotator-mixture for testing is never modeled explicitly in the training
phase, we propose to generate synthetic training samples by a pertinent mixup
strategy to make the training and testing highly consistent. The simulation
experiments on our constructed dataset show that crowdsourcing is highly
promising for OEI, and our proposed annotator-mixup can further enhance the
crowdsourcing modeling.
