Abstract
The paper describes SemEval-2022’s shared task “Intended Sarcasm Detection in English and Arabic.” This task includes English and Arabic tweets with sarcasm and non-sarcasm samples and irony speech labels.The first two subtasks predict whether a text is sarcastic and the ironic category the sarcasm sample belongs to. The third one is to find the sarcastic sample from its non-sarcastic paraphrase. Deep neural networks have recently achieved highly competitive performance in many tasks.Combining deep learning with language models has also resulted in acceptable accuracy. Inspired by this, we propose a novel deep learning model on top of language models. On top of T5, this architecture uses an encoder module of the transformer, followed by LSTM and attention to utilizing past and future information, concentrating on informative tokens. Due to the success of the proposed model, we used the same architecture with a few modifications to the output layer in all three subtasks.- Anthology ID:
- 2022.semeval-1.137
- 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:
- 978–986
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.137
- DOI:
- 10.18653/v1/2022.semeval-1.137
- Cite (ACL):
- Maryam Najafi and Ehsan Tavan. 2022. MarSan at SemEval-2022 Task 6: iSarcasm Detection via T5 and Sequence Learners. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 978–986, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- MarSan at SemEval-2022 Task 6: iSarcasm Detection via T5 and Sequence Learners (Najafi & Tavan, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.137.pdf
- Data
- iSarcasm