R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm

Mayukh Sharma, Ilanthenral Kandasamy, Vasantha W B


Abstract
This paper describes our system used for SemEval 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 distance-maximisation loss. Our model was ranked 7th in Subtask B, 8th 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.
Anthology ID:
2022.semeval-1.143
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1018–1024
Language:
URL:
https://aclanthology.org/2022.semeval-1.143
DOI:
10.18653/v1/2022.semeval-1.143
Bibkey:
Cite (ACL):
Mayukh Sharma, Ilanthenral Kandasamy, and Vasantha W B. 2022. R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1018–1024, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm (Sharma et al., SemEval 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/auto-file-uploads/2022.semeval-1.143.pdf
Data
iSarcasmiSarcasmEval