R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm
Mayukh Sharma, Ilanthenral Kandasamy, W. B. Vasantha · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
This paper describes our system used for Se-mEval 2022 Task 6: iSarcasmEval: Intended Sarcasm Detection in English and Arabic.We participated in all subtasks based on only English datasets.Pre-trained Language Models (PLMs) have become a de-facto approach for most natural language processing tasks.In our work, we evaluate the performance of these models for identifying sarcasm.For Subtask A and Subtask B, we used simple finetuning on PLMs.For Subtask C, we propose a Siamese network architecture trained using a combination of cross-entropy and distancemaximisation loss.Our model was ranked 7 th in Subtask B, 8 th in Subtask C (English), and performed well in Subtask A (English).In our work, we also present the comparative performance of different PLMs for each Subtask.