TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer Models
Ramdhanush V, Rajalakshmi Sivanaiah, Angel S, Sakaya Milton Rajendram, Mirnalinee T T
Abstract
Irony detection in the social media is an upcoming research which places a main role in sentiment analysis and offensive language identification. Sarcasm is one form of irony that is used to provide intended comments against realism. This paper describes a method to detect intended sarcasm in text (SemEval-2022 Task 6). The TECHSSN team used Bidirectional Encoder Representations from Transformers (BERT) models and its variants to classify the text as sarcastic or non-sarcastic in English and Arabic languages. The data is preprocessed and fed to the model for training. The transformer models learn the weights during the training phase from the given dataset and predicts the output class labels for the unseen test data.- Anthology ID:
- 2022.semeval-1.118
- 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:
- 851–855
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.118
- DOI:
- 10.18653/v1/2022.semeval-1.118
- Cite (ACL):
- Ramdhanush V, Rajalakshmi Sivanaiah, Angel S, Sakaya Milton Rajendram, and Mirnalinee T T. 2022. TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer Models. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 851–855, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer Models (V et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.118.pdf