@inproceedings{ammer-gruner-2020-unituebingencl,
    title = "{U}ni{T}uebingen{CL} at {S}em{E}val-2020 Task 7: Humor Detection in News Headlines",
    author = {Ammer, Charlotte  and
      Gr{\"u}ner, Lea},
    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/Add-Cong-Liu-Florida-Atlantic-University-author-id/2020.semeval-1.139/",
    doi = "10.18653/v1/2020.semeval-1.139",
    pages = "1060--1065",
    abstract = "This paper describes the work done by the team UniTuebingenCL for the SemEval 2020 Task 7: {\textquotedblleft}Assessing the Funniness of Edited News Headlines{\textquotedblright}. We participated in both sub-tasks: sub-task A, given the original and the edited headline, predicting the mean funniness of the edited headline; and sub-task B, given the original headline and two edited versions, predicting which edited version is the funnier of the two. A Ridge Regression model using Elmo and Glove embeddings as well as Truncated Singular Value Decomposition was used as the final model. A long short term memory model recurrent network (LSTM) served as another approach for assessing the funniness of a headline. For the first sub-task, we experimented with the extraction of multiple features to achieve lower Root Mean Squared Error. The lowest Root Mean Squared Error achieved was 0.575 for sub-task A, and the highest Accuracy was 0.618 for sub-task B."
}