We study the influence of different activation functions in the output layer of deep neural network models for soft and hard label prediction in the learning with disagreement task. In this task, the goal is to quantify the amount of disagreement via predicting soft labels. To predict the soft labels, we use BERT-based preprocessors and encoders and vary the activation function used in the output layer, while keeping other parameters constant. The soft labels are then used for the hard label prediction. The activation functions considered are sigmoid as well as a step-function that is added to the model post-training and a sinusoidal activation function, which is introduced for the first time in this paper.

翻译：我们研究了在分歧学习任务中，深度神经网络模型输出层不同激活函数对软标签和硬标签预测的影响。该任务旨在通过预测软标签来量化分歧程度。为预测软标签，我们使用基于BERT的预处理器和编码器，并在保持其他参数不变的情况下，改变输出层使用的激活函数。随后，软标签被用于硬标签预测。所考虑的激活函数包括sigmoid函数、训练后添加到模型中的阶跃函数，以及本文首次引入的正弦激活函数。