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.

本研究探讨了如何使用极其嘈杂的众包注释构建一个低成本的数据集，并通过注释器适配器模型和相关混合策略，提高了众包建模的准确性，结果表明众包在观点表达识别方面有很高的应用前景。