Besides, even if the model accurately predicts all true labels, it still may result in an unreasonable training loss due to the inconsistent order with the pre-defined label sequence 3 .
in this work, we propose a simple but effective sequence-to-set model, which aims at alleviating the dependence of the model on the label order.