@inproceedings{kumar-etal-2021-comma,
    title = "{C}om{MA}@{ICON}: Multilingual Gender Biased and Communal Language Identification Task at {ICON}-2021",
    author = "Kumar, Ritesh  and
      Ratan, Shyam  and
      Singh, Siddharth  and
      Nandi, Enakshi  and
      Devi, Laishram Niranjana  and
      Bhagat, Akash  and
      Dawer, Yogesh  and
      Lahiri, Bornini  and
      Bansal, Akanksha",
    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://aclanthology.org/2021.icon-multigen.1",
    pages = "1--12",
    abstract = "This paper presents the findings of the ICON-2021 shared task on Multilingual Gender Biased and Communal Language Identification, which aims to identify aggression, gender bias, and communal bias in data presented in four languages: Meitei, Bangla, Hindi and English. The participants were presented the option of approaching the task as three separate classification tasks or a multi-label classification task or a structured classification task. If approached as three separate classification tasks, the task includes three sub-tasks: aggression identification (sub-task A), gender bias identification (sub-task B), and communal bias identification (sub-task C). For this task, the participating teams were provided with a total dataset of approximately 12,000, with 3,000 comments across each of the four languages, sourced from popular social media sites such as YouTube, Twitter, Facebook and Telegram and the the three labels presented as a single tuple. For the test systems, approximately 1,000 comments were provided in each language for every sub-task. We attracted a total of 54 registrations in the task, out of which 11 teams submitted their test runs. The best system obtained an overall instance-F1 of 0.371 in the multilingual test set (it was simply a combined test set of the instances in each individual language). In the individual sub-tasks, the best micro f1 scores are 0.539, 0.767 and 0.834 respectively for each of the sub-task A, B and C. The best overall, averaged micro f1 is 0.713. The results show that while systems have managed to perform reasonably well in individual sub-tasks, especially gender bias and communal bias tasks, it is substantially more difficult to do a 3-class classification of aggression level and even more difficult to build a system that correctly classifies everything right. It is only in slightly over 1/3 of the instances that most of the systems predicted the correct class across the board, despite the fact that there was a significant overlap across the three sub-tasks.",
}