@inproceedings{baruah-etal-2020-iiitg-adbu-semeval,
    title = "{IIITG}-{ADBU} at {S}em{E}val-2020 Task 12: Comparison of {BERT} and {B}i{LSTM} in Detecting Offensive Language",
    author = "Baruah, Arup  and
      Das, Kaushik  and
      Barbhuiya, Ferdous  and
      Dey, Kuntal",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://aclanthology.org/2020.semeval-1.204",
    doi = "10.18653/v1/2020.semeval-1.204",
    pages = "1562--1568",
    abstract = "Task 12 of SemEval 2020 consisted of 3 subtasks, namely offensive language identification (Subtask A), categorization of offense type (Subtask B), and offense target identification (Subtask C). This paper presents the results our classifiers obtained for the English language in the 3 subtasks. The classifiers used by us were BERT and BiLSTM. On the test set, our BERT classifier obtained macro F1 score of 0.90707 for subtask A, and 0.65279 for subtask B. The BiLSTM classifier obtained macro F1 score of 0.57565 for subtask C. The paper also performs an analysis of the errors made by our classifiers. We conjecture that the presence of few misleading instances in the dataset is affecting the performance of the classifiers. Our analysis also discusses the need of temporal context and world knowledge to determine the offensiveness of few comments.",
}