@inproceedings{hosseini-etal-2023-lon,
    title = "Lon-e{\aa} at {S}em{E}val-2023 Task 11: A Comparison of Activation Functions for Soft and Hard Label Prediction",
    author = "Hosseini, Peyman  and
      Hosseini, Mehran  and
      Al-azzawi, Sana  and
      Liwicki, Marcus  and
      Castro, Ignacio  and
      Purver, Matthew",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.185",
    doi = "10.18653/v1/2023.semeval-1.185",
    pages = "1329--1334",
    abstract = "We study the influence of different activation functions in the output layer of pre-trained transformer 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.",
}

We study the influence of different activation functions in the output layer of pre-trained transformer 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.
Hosseini, Peyman Hosseini, Mehran Al-azzawi, Sana Liwicki, Marcus Castro, Ignacio Purver, Matthew
Lon-eå at SemEval-2023 Task 11: A Comparison of Activation Functions for Soft and Hard Label Prediction