Abstract
This study presents an ensemble approach for detecting sexist text in the context of the Semeval-2023 task 10. Our approach leverages 18 models, including DeBERTa-v3-base models with different input sequence lengths, a BERT-based model trained on identifying hate speech, and three more models pre-trained on the task’s unlabeled data with varying input lengths. The results of our framework on the development set show an f1-score of 84.92% and on the testing set 84.55%, effectively demonstrating the strength of the ensemble approach in getting accurate results.- Anthology ID:
- 2023.semeval-1.228
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1644–1649
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.228
- DOI:
- 10.18653/v1/2023.semeval-1.228
- Cite (ACL):
- Mutaz Younes, Ali Kharabsheh, and Mohammad Bani Younes. 2023. Alexa at SemEval-2023 Task 10: Ensemble Modeling of DeBERTa and BERT Variations for Identifying Sexist Text. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1644–1649, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Alexa at SemEval-2023 Task 10: Ensemble Modeling of DeBERTa and BERT Variations for Identifying Sexist Text (Younes et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.semeval-1.228.pdf