There are two competing approaches for modelling annotator disagreement:
distributional soft-labelling approaches (which aim to capture the level of
disagreement) or modelling perspectives of individual annotators or groups
thereof. We adapt a multi-task architecture -- which has previously shown
success in modelling perspectives -- to evaluate its performance on the SEMEVAL
Task 11. We do so by combining both approaches, i.e. predicting individual
annotator perspectives as an interim step towards predicting annotator
disagreement. Despite its previous success, we found that a multi-task approach
performed poorly on datasets which contained distinct annotator opinions,
suggesting that this approach may not always be suitable when modelling
perspectives. Furthermore, our results explain that while strongly
perspectivist approaches might not achieve state-of-the-art performance
according to evaluation metrics used by distributional approaches, our approach
allows for a more nuanced understanding of individual perspectives present in
the data. We argue that perspectivist approaches are preferable because they
enable decision makers to amplify minority views, and that it is important to
re-evaluate metrics to reflect this goal.

使用现有多任务模型评估并结合模型仿真方法与个体标注者观点来预测个体标注者的不一致性，得出具体少数观点的模型更适用于模型预测。