@inproceedings{moreno-monterde-etal-2022-i2c,
    title = "{I}2{C} at {S}em{E}val-2022 Task 6: Intended Sarcasm in {E}nglish using Deep Learning Techniques",
    author = "Moreno Monterde, Adri{\'a}n  and
      V{\'a}zquez Ramos, Laura  and
      Mata, Jacinto  and
      Pach{\'o}n {\'A}lvarez, Victoria",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.semeval-1.119/",
    doi = "10.18653/v1/2022.semeval-1.119",
    pages = "856--861",
    abstract = "Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has been carried out on textual data. This paper describes how the problem proposed in Task 6: Intended Sarcasm Detection in English (Abu Arfa et al. 2022) has been solved. Specifically, we participated in Subtask B: a binary multi-label classification task, where it is necessary to determine whether a tweet belongs to an ironic speech category, if any. Several approaches (classic machine learning and deep learning algorithms) were developed. The final submission consisted of a BERT based model and a macro-F1 score of 0.0699 was obtained."
}