Abstract
This paper presents our proposed methods for the iSarcasmEval shared task. The shared task consists of three different subtasks. We participate in both subtask A and subtask C. The purpose of subtask A was to predict if a text is sarcastic while the aim of subtask C is to determine which text is sarcastic given a sarcastic text and its non-sarcastic rephrase. Both of the developed solutions used BERT pre-trained models. The proposed models are optimized on simple objectives and are easy to grasp. However, despite their simplicity, our methods ranked 4 and 2 in iSarcasmEval subtask A and subtask C for Arabic texts.- Anthology ID:
- 2022.semeval-1.116
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 840–843
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.116
- DOI:
- 10.18653/v1/2022.semeval-1.116
- Cite (ACL):
- Hamza Alami, Abdessamad Benlahbib, and Ahmed Alami. 2022. High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 840–843, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts (Alami et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.semeval-1.116.pdf
- Data
- iSarcasmEval