@inproceedings{dutta-etal-2021-sdutta,
    title = "Sdutta at {C}om{MA}@{ICON}: A {CNN}-{LSTM} Model for Hate Detection",
    author = "Dutta, Sandip  and
      Majumder, Utso  and
      Naskar, Sudip",
    editor = "Kumar, Ritesh  and
      Singh, Siddharth  and
      Nandi, Enakshi  and
      Ratan, Shyam  and
      Devi, Laishram Niranjana  and
      Lahiri, Bornini  and
      Bansal, Akanksha  and
      Bhagat, Akash  and
      Dawer, Yogesh",
    booktitle = "Proceedings of the 18th International Conference on Natural Language Processing: Shared Task on Multilingual Gender Biased and Communal Language Identification",
    month = dec,
    year = "2021",
    address = "NIT Silchar",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.icon-multigen.8/",
    pages = "53--57",
    abstract = "In today{'}s world, online activity and social media are facing an upsurge of cases of aggression, gender-biased comments and communal hate. In this shared task, we used a CNN-LSTM hybrid method to detect aggression, misogynistic and communally charged content in social media texts. First, we employ text cleaning and convert the text into word embeddings. Next we proceed to our CNN-LSTM based model to predict the nature of the text. Our model achieves 0.288, 0.279, 0.294 and 0.335 Overall Micro F1 Scores in multilingual, Meitei, Bengali and Hindi datasets, respectively, on the 3 prediction labels."
}