Abstract
We present our systems and findings for the iSarcasmEval: Intended Sarcasm Detection In English and Arabic at SEMEVAL 2022. Specifically we take part in Subtask A for the English language. The task aims to determine whether a text from social media (a tweet) is sarcastic or not. We model the problem using knowledge sources, a pre-trained language model on sentiment/emotion data and a dataset focused on intended sarcasm. Our submission ranked third place among 43 teams. In addition, we show a brief error analysis of our best model to investigate challenging examples for detecting sarcasm.- Anthology ID:
- 2022.semeval-1.133
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 951–955
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.133
- DOI:
- 10.18653/v1/2022.semeval-1.133
- Cite (ACL):
- Jason Angel, Segun Aroyehun, and Alexander Gelbukh. 2022. TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 951–955, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection (Angel et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.semeval-1.133.pdf
- Data
- SPIRS, iSarcasmEval