Abstract
This paper describes team giniUs’ submission to the Hope Speech Detection for Equality, Diversity and Inclusion Shared Task organised by LT-EDI ACL 2022. We have fine-tuned the Roberta-large pre-trained model and extracted the last four decoder layers to build a classifier. Our best result on the leaderboard achieve a weighted F1 score of 0.86 and a Macro F1 score of 0.51 for English. We have secured a rank of 4 for the English task. We have open-sourced our code implementations on GitHub to facilitate easy reproducibility by the scientific community.- Anthology ID:
- 2022.ltedi-1.43
- Volume:
- Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- LTEDI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 291–295
- Language:
- URL:
- https://aclanthology.org/2022.ltedi-1.43
- DOI:
- 10.18653/v1/2022.ltedi-1.43
- Cite (ACL):
- Harshul Surana and Basavraj Chinagundi. 2022. giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 291–295, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI (Surana & Chinagundi, LTEDI 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.ltedi-1.43.pdf
- Data
- HopeEDI