@inproceedings{miraj-aono-2020-kdehumor,
    title = "{KDE}humor at {S}em{E}val-2020 Task 7: A Neural Network Model for Detecting Funniness in Dataset Humicroedit",
    author = "Miraj, Rida  and
      Aono, Masaki",
    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/fix-sig-urls/2020.semeval-1.107/",
    doi = "10.18653/v1/2020.semeval-1.107",
    pages = "852--857",
    abstract = "This paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News Headlines. Here we present a method based on a deep neural network. In recent years, quite some attention has been devoted to humor production and perception. Our team KDEhumor employs recurrent neural network models including Bi-Directional LSTMs (BiLSTMs). Moreover, we utilize the state-of-the-art pre-trained sentence embedding techniques. We analyze the performance of our method and demonstrate the contribution of each component of our architecture."
}