@inproceedings{soloveva-2020-semeval,
    title = "{SO} at {S}em{E}val-2020 Task 7: {D}eep{P}avlov Logistic Regression with {BERT} Embeddings vs {SVR} at Funniness Evaluation",
    author = "Soloveva, Anita",
    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-1.138/",
    doi = "10.18653/v1/2020.semeval-1.138",
    pages = "1055--1059",
    abstract = "This paper describes my efforts in evaluating how editing news headlines can make them funnier within the frames of SemEval 2020 Task 7. I participated in both of the sub-tasks: Sub-Task 1 {\textquotedblleft}Regression{\textquotedblright} and Sub-task 2 {\textquotedblleft}Predict the funnier of the two edited versions of an original headline{\textquotedblright}. I experimented with a number of different models, but ended up using DeepPavlov logistic regression (LR) with BERT English cased embeddings for the first sub-task and support vector regression model (SVR) for the second. RMSE score obtained for the first task was 0.65099 and accuracy for the second {--} 0.32915."
}