Abstract
Toxic spans detection is an emerging challenge that aims to find toxic spans within a toxic text. In this paper, we describe our solutions to tackle toxic spans detection. The first solution, which follows a supervised approach, is based on SpanBERT model. This latter is intended to better embed and predict spans of text. The second solution, which adopts an unsupervised approach, combines linear support vector machine with the Local Interpretable Model-Agnostic Explanations (LIME). This last is used to interpret predictions of learning-based models. Our supervised model outperformed the unsupervised model and achieved the f-score of 67,84% (ranked 22/85) in Task 5 at SemEval-2021: Toxic Spans Detection.- Anthology ID:
- 2021.semeval-1.116
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 865–869
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.116
- DOI:
- 10.18653/v1/2021.semeval-1.116
- Cite (ACL):
- Abdessamad Benlahbib, Ahmed Alami, and Hamza Alami. 2021. LISAC FSDM USMBA at SemEval-2021 Task 5: Tackling Toxic Spans Detection Challenge with Supervised SpanBERT-based Model and Unsupervised LIME-based Model. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 865–869, Online. Association for Computational Linguistics.
- Cite (Informal):
- LISAC FSDM USMBA at SemEval-2021 Task 5: Tackling Toxic Spans Detection Challenge with Supervised SpanBERT-based Model and Unsupervised LIME-based Model (Benlahbib et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.semeval-1.116.pdf