Abstract
In this paper, we present our system and findings for SemEval-2022 Task 6 - iSarcasmEval: Intended Sarcasm Detection in English. The main objective of this task was to identify sarcastic tweets. This task was challenging mainly due to (1) the small training dataset that contains only 3468 tweets and (2) the imbalanced class distribution (25% sarcastic and 75% non-sarcastic). Our submitted model (ranked eighth on Sub-Task A and fifth on Sub-Task C) consists of a Transformer-based approach (BERTweet model).- Anthology ID:
- 2022.semeval-1.139
- 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:
- 993–998
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.139
- DOI:
- 10.18653/v1/2022.semeval-1.139
- Cite (ACL):
- Abdessamad Benlahbib, Hamza Alami, and Ahmed Alami. 2022. LISACTeam at SemEval-2022 Task 6: A Transformer based Approach for Intended Sarcasm Detection in English Tweets. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 993–998, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- LISACTeam at SemEval-2022 Task 6: A Transformer based Approach for Intended Sarcasm Detection in English Tweets (Benlahbib et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.semeval-1.139.pdf
- Data
- iSarcasmEval