@inproceedings{rozental-etal-2020-amobee,
    title = "{A}mobee at {S}em{E}val-2020 Task 7: Regularization of Language Model Based Classifiers",
    author = "Rozental, Alon  and
      Biton, Dadi  and
      Blank, Ido",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.127/",
    doi = "10.18653/v1/2020.semeval-1.127",
    pages = "981--985",
    abstract = "This paper describes Amobee`s participation in SemEval-2020 task 7: {\textquotedblleft}Assessing Humor in Edited News Headlines{\textquotedblright}, sub-tasks 1 and 2. The goal of this task was to estimate the funniness of human modified news headlines. in this paper we present methods to fine-tune and ensemble various language models (LM) based classifiers to for this task. This technique used for both sub-tasks and reached the second place (out of 49) in sub-tasks 1 with RMSE score of 0.5, and the second (out of 32) place in sub-task 2 with accuracy of 66{\%} without using any additional data except the official training set."
}