SRPOL DIALOGUE SYSTEMS at SemEval-2021 Task 5: Automatic Generation of Training Data for Toxic Spans Detection
Michał Satława, Katarzyna Zamłyńska, Jarosław Piersa, Joanna Kolis, Klaudia Firląg, Katarzyna Beksa, Zuzanna Bordzicka, Christian Goltz, Paweł Bujnowski, Piotr Andruszkiewicz
Abstract
This paper presents a system used for SemEval-2021 Task 5: Toxic Spans Detection. Our system is an ensemble of BERT-based models for binary word classification, trained on a dataset extended by toxic comments modified and generated by two language models. For the toxic word classification, the prediction threshold value was optimized separately for every comment, in order to maximize the expected F1 value.- Anthology ID:
- 2021.semeval-1.133
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 974–983
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.133
- DOI:
- 10.18653/v1/2021.semeval-1.133
- Cite (ACL):
- Michał Satława, Katarzyna Zamłyńska, Jarosław Piersa, Joanna Kolis, Klaudia Firląg, Katarzyna Beksa, Zuzanna Bordzicka, Christian Goltz, Paweł Bujnowski, and Piotr Andruszkiewicz. 2021. SRPOL DIALOGUE SYSTEMS at SemEval-2021 Task 5: Automatic Generation of Training Data for Toxic Spans Detection. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 974–983, Online. Association for Computational Linguistics.
- Cite (Informal):
- SRPOL DIALOGUE SYSTEMS at SemEval-2021 Task 5: Automatic Generation of Training Data for Toxic Spans Detection (Satława et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.semeval-1.133.pdf