Lon-ea at SemEval-2023 Task 11: A Comparison of Activation Functions for Soft and Hard Label Prediction
conf_acl(2023)
摘要
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.
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关键词
hard label prediction,activation functions
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