@inproceedings{salemi-etal-2021-utnlp,
title = "{UTNLP} at {S}em{E}val-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models",
author = "Salemi, Alireza and
Sabri, Nazanin and
Kebriaei, Emad and
Bahrak, Behnam and
Shakery, Azadeh",
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-1.136",
doi = "10.18653/v1/2021.semeval-1.136",
pages = "995--1002",
abstract = "Detecting which parts of a sentence contribute to that sentence{'}s toxicity{---}rather than providing a sentence-level verdict of hatefulness{---} would increase the interpretability of models and allow human moderators to better understand the outputs of the system. This paper presents our team{'}s, UTNLP, methodology and results in the SemEval-2021 shared task 5 on toxic spans detection. We test multiple models and contextual embeddings and report the best setting out of all. The experiments start with keyword-based models and are followed by attention-based, named entity- based, transformers-based, and ensemble models. Our best approach, an ensemble model, achieves an F1 of 0.684 in the competition{'}s evaluation phase.",
}
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<abstract>Detecting which parts of a sentence contribute to that sentence’s toxicity—rather than providing a sentence-level verdict of hatefulness— would increase the interpretability of models and allow human moderators to better understand the outputs of the system. This paper presents our team’s, UTNLP, methodology and results in the SemEval-2021 shared task 5 on toxic spans detection. We test multiple models and contextual embeddings and report the best setting out of all. The experiments start with keyword-based models and are followed by attention-based, named entity- based, transformers-based, and ensemble models. Our best approach, an ensemble model, achieves an F1 of 0.684 in the competition’s evaluation phase.</abstract>
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%0 Conference Proceedings
%T UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models
%A Salemi, Alireza
%A Sabri, Nazanin
%A Kebriaei, Emad
%A Bahrak, Behnam
%A Shakery, Azadeh
%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 salemi-etal-2021-utnlp
%X Detecting which parts of a sentence contribute to that sentence’s toxicity—rather than providing a sentence-level verdict of hatefulness— would increase the interpretability of models and allow human moderators to better understand the outputs of the system. This paper presents our team’s, UTNLP, methodology and results in the SemEval-2021 shared task 5 on toxic spans detection. We test multiple models and contextual embeddings and report the best setting out of all. The experiments start with keyword-based models and are followed by attention-based, named entity- based, transformers-based, and ensemble models. Our best approach, an ensemble model, achieves an F1 of 0.684 in the competition’s evaluation phase.
%R 10.18653/v1/2021.semeval-1.136
%U https://aclanthology.org/2021.semeval-1.136
%U https://doi.org/10.18653/v1/2021.semeval-1.136
%P 995-1002
Markdown (Informal)
[UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models](https://aclanthology.org/2021.semeval-1.136) (Salemi et al., SemEval 2021)
ACL