@inproceedings{mosquera-2022-amsqr,
    title = "Amsqr at {S}em{E}val-2022 Task 4: Towards {A}uto{NLP} via Meta-Learning and Adversarial Data Augmentation for {PCL} Detection",
    author = "Mosquera, Alejandro",
    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/fix-sig-urls/2022.semeval-1.66/",
    doi = "10.18653/v1/2022.semeval-1.66",
    pages = "485--489",
    abstract = "This paper describes the use of AutoNLP techniques applied to the detection of patronizing and condescending language (PCL) in a binary classification scenario. The proposed approach combines meta-learning, in order to identify the best performing combination of deep learning architectures, with the synthesis of adversarial training examples; thus boosting robustness and model generalization. A submission from this system was evaluated as part of the first sub-task of SemEval 2022 - Task 4 and achieved an F1 score of 0.57{\%}, which is 16 percentage points higher than the RoBERTa baseline provided by the organizers."
}