Abstract
In this paper, we present UFAL-ULD team’s system, desinged as a part of the BLP Shared Task 1: Violence Inciting Text Detection (VITD). This task aims to classify text, with a particular challenge of identifying incitement to violence into Direct, Indirect or Non-violence levels. We experimented with several pre-trained sequence classification models, including XLM-RoBERTa, BanglaBERT, Bangla BERT Base, and Multilingual BERT. Our best-performing model was based on the XLM-RoBERTa-base architecture, which outperformed the baseline models. Our system was ranked 20th among the 27 teams that participated in the task.- Anthology ID:
- 2023.banglalp-1.27
- Volume:
- Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Firoj Alam, Sudipta Kar, Shammur Absar Chowdhury, Farig Sadeque, Ruhul Amin
- Venue:
- BanglaLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 220–224
- Language:
- URL:
- https://aclanthology.org/2023.banglalp-1.27
- DOI:
- 10.18653/v1/2023.banglalp-1.27
- Cite (ACL):
- Sourabrata Mukherjee, Atul Kr. Ojha, and Ondřej Dušek. 2023. UFAL-ULD at BLP-2023 Task 1: Violence Detection in Bangla Text. In Proceedings of the First Workshop on Bangla Language Processing (BLP-2023), pages 220–224, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- UFAL-ULD at BLP-2023 Task 1: Violence Detection in Bangla Text (Mukherjee et al., BanglaLP 2023)
- PDF:
- https://preview.aclanthology.org/landing_page/2023.banglalp-1.27.pdf