@inproceedings{amihaesei-etal-2023-appeal,
    title = "Appeal for Attention at {S}em{E}val-2023 Task 3: Data augmentation extension strategies for detection of online news persuasion techniques",
    author = "Amihaesei, Sergiu  and
      Cornei, Laura  and
      Stoica, George",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.84",
    doi = "10.18653/v1/2023.semeval-1.84",
    pages = "616--623",
    abstract = "In this paper, we proposed and explored the impact of four different dataset augmentation andextension strategies that we used for solving the subtask 3 of SemEval-2023 Task 3: multi-label persuasion techniques classification in a multi-lingual context. We consider two types of augmentation methods (one based on a modified version of synonym replacement and one based on translations) and two ways of extending the training dataset (using filtered data generated by GPT-3 and using a dataset from a previous competition). We studied the effects of the aforementioned techniques by using theaugmented and/or extended training dataset to fine-tune a pretrained XLM-RoBERTa-Large model. Using the augmentation methods alone, we managed to obtain 3rd place for English, 13th place for Italian and between the 5th to 9th places for the other 7 languages during the competition.",
}

In this paper, we proposed and explored the impact of four different dataset augmentation andextension strategies that we used for solving the subtask 3 of SemEval-2023 Task 3: multi-label persuasion techniques classification in a multi-lingual context. We consider two types of augmentation methods (one based on a modified version of synonym replacement and one based on translations) and two ways of extending the training dataset (using filtered data generated by GPT-3 and using a dataset from a previous competition). We studied the effects of the aforementioned techniques by using theaugmented and/or extended training dataset to fine-tune a pretrained XLM-RoBERTa-Large model. Using the augmentation methods alone, we managed to obtain 3rd place for English, 13th place for Italian and between the 5th to 9th places for the other 7 languages during the competition.
Amihaesei, Sergiu Cornei, Laura Stoica, George
Appeal for Attention at SemEval-2023 Task 3: Data augmentation extension strategies for detection of online news persuasion techniques