@inproceedings{li-etal-2020-lee,
    title = "Lee at {S}em{E}val-2020 Task 5: {ALBERT} Model Based on the Maximum Ensemble Strategy and Different Data Sampling Methods for Detecting Counterfactual Statements",
    author = "Li, Junyi  and
      Wu, Yuhang  and
      Wang, Bin  and
      Ding, Haiyan",
    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/Add-Cong-Liu-Florida-Atlantic-University-author-id/2020.semeval-1.86/",
    doi = "10.18653/v1/2020.semeval-1.86",
    pages = "664--669",
    abstract = "This article describes the system submitted to SemEval 2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. In this task, we only participate in the subtask A which is detecting counterfactual statements. In order to solve this sub-task, first of all, because of the problem of data balance, we use the undersampling and oversampling methods to process the data set. Second, we used the ALBERT model and the maximum ensemble method based on the ALBERT model. Our methods achieved a F1 score of 0.85 in subtask A."
}