Abstract
In recent years, with the development of social network services and video distribution services, there has been a sharp increase in offensive posts. In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval- 2020 Task 12. Our system precise classification by using features extracted from two different layers of a pre-trained model, the BERT-large, and ensemble them.- Anthology ID:
- 2020.semeval-1.268
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 2030–2034
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.268
- DOI:
- 10.18653/v1/2020.semeval-1.268
- Cite (ACL):
- Keisuke Hanahata and Masaki Aono. 2020. KDELAB at SemEval-2020 Task 12: A System for Estimating Aggression of Tweets Using Two Layers of BERT Features. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2030–2034, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- KDELAB at SemEval-2020 Task 12: A System for Estimating Aggression of Tweets Using Two Layers of BERT Features (Hanahata & Aono, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2020.semeval-1.268.pdf