@inproceedings{maity-etal-2023-irel,
    title = "{IREL} at {S}em{E}val-2023 Task 11: User Conditioned Modelling for Toxicity Detection in Subjective Tasks",
    author = "Maity, Ankita  and
      Kandru, Pavan  and
      Singh, Bhavyajeet  and
      Aditya Hari, Kancharla  and
      Varma, Vasudeva",
    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-1.294",
    doi = "10.18653/v1/2023.semeval-1.294",
    pages = "2133--2136",
    abstract = "This paper describes our system used in the SemEval-2023 Task 11 Learning With Disagreements (Le-Wi-Di). This is a subjective task since it deals with detecting hate speech, misogyny and offensive language. Thus, disagreement among annotators is expected. We experiment with different settings like loss functions specific for subjective tasks and include anonymized annotator-specific information to help us understand the level of disagreement. We perform an in-depth analysis of the performance discrepancy of these different modelling choices. Our system achieves a cross-entropy of 0.58, 4.01 and 3.70 on the test sets of HS-Brexit, ArMIS and MD-Agreement, respectively. Our code implementation is publicly available.",
}

This paper describes our system used in the SemEval-2023 Task 11 Learning With Disagreements (Le-Wi-Di). This is a subjective task since it deals with detecting hate speech, misogyny and offensive language. Thus, disagreement among annotators is expected. We experiment with different settings like loss functions specific for subjective tasks and include anonymized annotator-specific information to help us understand the level of disagreement. We perform an in-depth analysis of the performance discrepancy of these different modelling choices. Our system achieves a cross-entropy of 0.58, 4.01 and 3.70 on the test sets of HS-Brexit, ArMIS and MD-Agreement, respectively. Our code implementation is publicly available.
Maity, Ankita K ru, Pavan Singh, Bhavyajeet Aditya Hari, Kancharla Varma, Vasudeva
IREL at SemEval-2023 Task 11: User Conditioned Modelling for Toxicity Detection in Subjective Tasks