I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning
Pablo Gonzalez Diaz, Pablo Cordon, Jacinto Mata, Victoria Pachón
Abstract
In this paper we present our approach and system description on iSarcasmEval: a SemEval task for intended sarcasm detection on social networks. This derives from our participation in SubTask A: Given a text, determine whether it is sarcastic or non-sarcastic. In our approach to complete the task, a comparison of several machine learning and deep learning algorithms using two datasets was conducted. The model which obtained the highest values of F1-score was a BERT-base-cased model. With this one, an F1-score of 0.2451 for the sarcastic class in the evaluation process was achieved. Finally, our team reached the 30th position.- Anthology ID:
- 2022.semeval-1.122
- 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:
- 877–880
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.122
- DOI:
- 10.18653/v1/2022.semeval-1.122
- Cite (ACL):
- Pablo Gonzalez Diaz, Pablo Cordon, Jacinto Mata, and Victoria Pachón. 2022. I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 877–880, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning (Gonzalez Diaz et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.122.pdf
- Data
- iSarcasmEval