reamtchka at SemEval-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasets
Reem Abdel‐Salam · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
This paper describes the system used in SemEval-2022 Task 6: Intended Sarcasm Detection in English and Arabic.Achieving 20th, 3rd places with 34& 47 F1-Sarcastic score for task A, 16th place for task B with 0.0560 F1macro score, and 10, 6th places for task C with 72% and 80% accuracy on the leaderboard.A voting classifier between either multiple different BERT-based models or machine learning models is proposed, as our final model.Multiple key points have been extensively examined to overcome the problem of the unbalance of the dataset as: type of models, suitable architecture, augmentation, loss function, etc.In addition to that, we present an analysis of our results in this work, highlighting its strengths and shortcomings.