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WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans

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WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans. / Ranasinghe, Tharindu; Sarkar, Diptanu; Zampieri, Marcos et al.
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021). Association for Computational Linguistics, 2021. p. 833-840.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Ranasinghe, T, Sarkar, D, Zampieri, M & Ororbia, A 2021, WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans. in Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021). Association for Computational Linguistics, pp. 833-840, 15th International Workshop on Semantic Evaluation (SemEval-2021), 1/08/21. https://doi.org/10.18653/v1/2021.semeval-1.111

APA

Ranasinghe, T., Sarkar, D., Zampieri, M., & Ororbia, A. (2021). WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021) (pp. 833-840). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.semeval-1.111

Vancouver

Ranasinghe T, Sarkar D, Zampieri M, Ororbia A. WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021). Association for Computational Linguistics. 2021. p. 833-840 doi: 10.18653/v1/2021.semeval-1.111

Author

Ranasinghe, Tharindu ; Sarkar, Diptanu ; Zampieri, Marcos et al. / WLV-RIT at SemEval-2021 Task 5 : A Neural Transformer Framework for Detecting Toxic Spans. Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021). Association for Computational Linguistics, 2021. pp. 833-840

Bibtex

@inproceedings{0276837284454a5bb6f1c1a31b105547,
title = "WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans",
abstract = "In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. In response, social media platforms have worked on developing automatic detection methods and employing human moderators to cope with this deluge of offensive content. While various state-of-the-art statistical models have been applied to detect toxic posts, there are only a few studies that focus on detecting the words or expressions that make a post offensive. This motivates the organization of the SemEval-2021 Task 5: Toxic Spans Detection competition, which has provided participants with a dataset containing toxic spans annotation in English posts. In this paper, we present the WLV-RIT entry for the SemEval-2021 Task 5. Our best performing neural transformer model achieves an 0.68 F1-Score. Furthermore, we develop an open-source framework for multilingual detection of offensive spans, i.e., MUDES, based on neural transformers that detect toxic spans in texts.",
author = "Tharindu Ranasinghe and Diptanu Sarkar and Marcos Zampieri and Alex Ororbia",
year = "2021",
month = aug,
day = "5",
doi = "10.18653/v1/2021.semeval-1.111",
language = "English",
isbn = "9781954085701",
pages = "833--840",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
publisher = "Association for Computational Linguistics",
note = "15th International Workshop on Semantic Evaluation (SemEval-2021) ; Conference date: 01-08-2021",

}

RIS

TY - GEN

T1 - WLV-RIT at SemEval-2021 Task 5

T2 - 15th International Workshop on Semantic Evaluation (SemEval-2021)

AU - Ranasinghe, Tharindu

AU - Sarkar, Diptanu

AU - Zampieri, Marcos

AU - Ororbia, Alex

PY - 2021/8/5

Y1 - 2021/8/5

N2 - In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. In response, social media platforms have worked on developing automatic detection methods and employing human moderators to cope with this deluge of offensive content. While various state-of-the-art statistical models have been applied to detect toxic posts, there are only a few studies that focus on detecting the words or expressions that make a post offensive. This motivates the organization of the SemEval-2021 Task 5: Toxic Spans Detection competition, which has provided participants with a dataset containing toxic spans annotation in English posts. In this paper, we present the WLV-RIT entry for the SemEval-2021 Task 5. Our best performing neural transformer model achieves an 0.68 F1-Score. Furthermore, we develop an open-source framework for multilingual detection of offensive spans, i.e., MUDES, based on neural transformers that detect toxic spans in texts.

AB - In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. In response, social media platforms have worked on developing automatic detection methods and employing human moderators to cope with this deluge of offensive content. While various state-of-the-art statistical models have been applied to detect toxic posts, there are only a few studies that focus on detecting the words or expressions that make a post offensive. This motivates the organization of the SemEval-2021 Task 5: Toxic Spans Detection competition, which has provided participants with a dataset containing toxic spans annotation in English posts. In this paper, we present the WLV-RIT entry for the SemEval-2021 Task 5. Our best performing neural transformer model achieves an 0.68 F1-Score. Furthermore, we develop an open-source framework for multilingual detection of offensive spans, i.e., MUDES, based on neural transformers that detect toxic spans in texts.

U2 - 10.18653/v1/2021.semeval-1.111

DO - 10.18653/v1/2021.semeval-1.111

M3 - Conference contribution/Paper

SN - 9781954085701

SP - 833

EP - 840

BT - Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

PB - Association for Computational Linguistics

Y2 - 1 August 2021

ER -