@inproceedings{gia-hoang-etal-2021-uit,
title = "{UIT}-E10dot3 at {S}em{E}val-2021 Task 5: Toxic Spans Detection with Named Entity Recognition and Question-Answering Approaches",
author = "Gia Hoang, Phu and
Thanh Nguyen, Luan and
Nguyen, Kiet",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.125",
doi = "10.18653/v1/2021.semeval-1.125",
pages = "919--926",
abstract = "The increment of toxic comments on online space is causing tremendous effects on other vulnerable users. For this reason, considerable efforts are made to deal with this, and SemEval-2021 Task 5: Toxic Spans Detection is one of those. This task asks competitors to extract spans that have toxicity from the given texts, and we have done several analyses to understand its structure before doing experiments. We solve this task by two approaches, Named Entity Recognition with spaCy{'}s library and Question-Answering with RoBERTa combining with ToxicBERT, and the former gains the highest F1-score of 66.99{\%}.",
}
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<abstract>The increment of toxic comments on online space is causing tremendous effects on other vulnerable users. For this reason, considerable efforts are made to deal with this, and SemEval-2021 Task 5: Toxic Spans Detection is one of those. This task asks competitors to extract spans that have toxicity from the given texts, and we have done several analyses to understand its structure before doing experiments. We solve this task by two approaches, Named Entity Recognition with spaCy’s library and Question-Answering with RoBERTa combining with ToxicBERT, and the former gains the highest F1-score of 66.99%.</abstract>
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%0 Conference Proceedings
%T UIT-E10dot3 at SemEval-2021 Task 5: Toxic Spans Detection with Named Entity Recognition and Question-Answering Approaches
%A Gia Hoang, Phu
%A Thanh Nguyen, Luan
%A Nguyen, Kiet
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F gia-hoang-etal-2021-uit
%X The increment of toxic comments on online space is causing tremendous effects on other vulnerable users. For this reason, considerable efforts are made to deal with this, and SemEval-2021 Task 5: Toxic Spans Detection is one of those. This task asks competitors to extract spans that have toxicity from the given texts, and we have done several analyses to understand its structure before doing experiments. We solve this task by two approaches, Named Entity Recognition with spaCy’s library and Question-Answering with RoBERTa combining with ToxicBERT, and the former gains the highest F1-score of 66.99%.
%R 10.18653/v1/2021.semeval-1.125
%U https://aclanthology.org/2021.semeval-1.125
%U https://doi.org/10.18653/v1/2021.semeval-1.125
%P 919-926
Markdown (Informal)
[UIT-E10dot3 at SemEval-2021 Task 5: Toxic Spans Detection with Named Entity Recognition and Question-Answering Approaches](https://aclanthology.org/2021.semeval-1.125) (Gia Hoang et al., SemEval 2021)
ACL