Abstract
This paper describes our contribution to SemEval-2021 Task 5: Toxic Spans Detection. Our approach considers toxic spans detection as a segmentation problem. The system, Waw-unet, consists of a 1-D convolutional neural network adopted from U-Net architecture commonly applied for semantic segmentation. We customize existing architecture by adding a special network block considering for text segmentation, as an essential component of the model. We compared the model with two transformers-based systems RoBERTa and XLM-RoBERTa to see its performance against pre-trained language models. We obtained 0.6251 f1 score with Waw-unet while 0.6390 and 0.6601 with the compared models respectively.- Anthology ID:
- 2021.semeval-1.123
- 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:
- 909–912
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.123
- DOI:
- 10.18653/v1/2021.semeval-1.123
- Cite (ACL):
- Selman Delil, Birol Kuyumcu, and Cüneyt Aksakallı. 2021. Sefamerve ARGE at SemEval-2021 Task 5: Toxic Spans Detection Using Segmentation Based 1-D Convolutional Neural Network Model. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 909–912, Online. Association for Computational Linguistics.
- Cite (Informal):
- Sefamerve ARGE at SemEval-2021 Task 5: Toxic Spans Detection Using Segmentation Based 1-D Convolutional Neural Network Model (Delil et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.semeval-1.123.pdf
- Code
- birolkuyumcu/wawunet_for_toxicspan