## 2 Related Work
The subtasks for English, Hindi, and Marathi include two classification tasks: a binary classification task, in which systems classify tweets as containing Hateful or Offensive content (HOF) or Not (NOT), as well as a fine-grained classification task for English and Hindi, with three classes: HATE (hate speech), OFFENSIVE (offensive content), and PROFANITY (profanity). The data for this competition was gathered from Twitter and annotated by experts. The participating teams utilized a range of technologies in this shared task. Several teams used transformer-based models, including BERT, XLM-R [16], or Multilingual-BERT, and these models generally achieved high scores in the classification tasks. Additionally, some teams utilized machine learning models and obtained good results. However, transformer-based models appear to be particularly successful when used for this task, with the best-performing algorithms being mainly transformer variants. The overall highest F1 scores for the classification of hate speech in Marathi, Hindi, and English were 0.91, 0.78, and 0.83, respectively. For instance, one team employed a GCN-based approach in the Hindi subtask, which involves defining tweets and words as nodes and connecting them with sliding window relationships. They obtained a Macro F1 score of 0.8215 for Task A. Another team used a soft-voting ensemble of four different models in the English subtask, and obtained a Macro F1 score of 0.8215 for Task A. In general, the results of the HASOC competition indicate that both transformer-based and machine-learning models can effectively detect hate speech and other offensive content in social media. Despite this progress, significant work must be done, in order to improve NLP models for low-resource languages. For example, current pre-trained transformer models are typically trained on data from the internet and can be biased towards particular dialects or cultural groups. Additionally, many low-resource languages have unique characteristics, such as complex morphological structures and different writing systems, making it more difficult to develop models that accurately process and understand the language. An example of this can be seen in the shared task ([[https://sites.google.com/view/multi-label-emotionsfire-task/](https://sites.google.com/view/multi-label-emotionsfire-task/)]([https://sites.google.com/view/multi-label-emotionsfire-task/](https://sites.google.com/view/multi-label-emotionsfire-task/)) (accessed on 6 February 2023)) conducted on \"Emotions and Threat detection in the Urdu language\" [17; 18]. Most of the submitted models used multilingual transformers, yet had much room for improvement due to confusion surrounding multiple emotions, i.e., sadness and surprise. Hence, producing quality datasets and analyses for understanding the range of emotions is necessary, in order to develop transformer methods further. To better understand the contribution of transformers, specifically in terms of the sentiment-related tasks, we present Table 1, which identifies the transformer method, F1-score, task, and dataset dimensions used in the study. The table results also reaffirm our hypothesis regarding the transformer method's detection of fine-grained sentiments in English. With different dataset dimensions for several tasks, we see that transformers dominate the results. However, since transformer methods have not been tested on hope and regret in a multi-class setting, such as that proposed in this paper, it is important to see if they can outperform existing state-of-the-art datasets, as well as if they can be compared to the performance of other related emotion-based tasks.