@inproceedings{lekshmiammal-etal-2022-nitk,
title = "{NITK}-{IT}{\_}{NLP}@{T}amil{NLP}-{ACL}2022: Transformer based model for Toxic Span Identification in {T}amil",
author = "LekshmiAmmal, Hariharan and
Ravikiran, Manikandan and
Madasamy, Anand Kumar",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Krishnamurthy, Parameswari and
Sherly, Elizabeth and
Mahesan, Sinnathamby",
booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dravidianlangtech-1.12",
doi = "10.18653/v1/2022.dravidianlangtech-1.12",
pages = "75--78",
abstract = "Toxic span identification in Tamil is a shared task that focuses on identifying harmful content, contributing to offensiveness. In this work, we have built a model that can efficiently identify the span of text contributing to offensive content. We have used various transformer-based models to develop the system, out of which the fine-tuned MuRIL model was able to achieve the best overall character F1-score of 0.4489.",
}
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%0 Conference Proceedings
%T NITK-IT_NLP@TamilNLP-ACL2022: Transformer based model for Toxic Span Identification in Tamil
%A LekshmiAmmal, Hariharan
%A Ravikiran, Manikandan
%A Madasamy, Anand Kumar
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%Y Mahesan, Sinnathamby
%S Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lekshmiammal-etal-2022-nitk
%X Toxic span identification in Tamil is a shared task that focuses on identifying harmful content, contributing to offensiveness. In this work, we have built a model that can efficiently identify the span of text contributing to offensive content. We have used various transformer-based models to develop the system, out of which the fine-tuned MuRIL model was able to achieve the best overall character F1-score of 0.4489.
%R 10.18653/v1/2022.dravidianlangtech-1.12
%U https://aclanthology.org/2022.dravidianlangtech-1.12
%U https://doi.org/10.18653/v1/2022.dravidianlangtech-1.12
%P 75-78
Markdown (Informal)
[NITK-IT_NLP@TamilNLP-ACL2022: Transformer based model for Toxic Span Identification in Tamil](https://aclanthology.org/2022.dravidianlangtech-1.12) (LekshmiAmmal et al., DravidianLangTech 2022)
ACL