@inproceedings{ravikiran-etal-2023-findings,
title = "Findings of the Second Shared Task on Offensive Span Identification from Code-Mixed {T}amil-{E}nglish Comments",
author = "Ravikiran, Manikandan and
Ganesh, Ananth and
M, Anand Kumar and
Rajalakshmi, R and
Chakravarthi, Bharathi Raja",
editor = "Chakravarthi, Bharathi R. and
Priyadharshini, Ruba and
M, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth",
booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.dravidianlangtech-1.7",
pages = "52--58",
abstract = "Maintaining effective control over offensive content is essential on social media platforms to foster constructive online discussions. Yet, when it comes to code-mixed Dravidian languages, the current prevalence of offensive content moderation is restricted to categorizing entire comments, failing to identify specific portions that contribute to the offensiveness. Such limitation is primarily due to the lack of annotated data and open source systems for offensive spans. To alleviate this issue, in this shared task, we offer a collection of Tamil-English code-mixed social comments that include offensive comments. This paper provides an overview of the released dataset, the algorithms employed, and the outcomes achieved by the systems submitted for this task.",
}
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<abstract>Maintaining effective control over offensive content is essential on social media platforms to foster constructive online discussions. Yet, when it comes to code-mixed Dravidian languages, the current prevalence of offensive content moderation is restricted to categorizing entire comments, failing to identify specific portions that contribute to the offensiveness. Such limitation is primarily due to the lack of annotated data and open source systems for offensive spans. To alleviate this issue, in this shared task, we offer a collection of Tamil-English code-mixed social comments that include offensive comments. This paper provides an overview of the released dataset, the algorithms employed, and the outcomes achieved by the systems submitted for this task.</abstract>
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%0 Conference Proceedings
%T Findings of the Second Shared Task on Offensive Span Identification from Code-Mixed Tamil-English Comments
%A Ravikiran, Manikandan
%A Ganesh, Ananth
%A M, Anand Kumar
%A Rajalakshmi, R.
%A Chakravarthi, Bharathi Raja
%Y Chakravarthi, Bharathi R.
%Y Priyadharshini, Ruba
%Y M, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%S Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F ravikiran-etal-2023-findings
%X Maintaining effective control over offensive content is essential on social media platforms to foster constructive online discussions. Yet, when it comes to code-mixed Dravidian languages, the current prevalence of offensive content moderation is restricted to categorizing entire comments, failing to identify specific portions that contribute to the offensiveness. Such limitation is primarily due to the lack of annotated data and open source systems for offensive spans. To alleviate this issue, in this shared task, we offer a collection of Tamil-English code-mixed social comments that include offensive comments. This paper provides an overview of the released dataset, the algorithms employed, and the outcomes achieved by the systems submitted for this task.
%U https://aclanthology.org/2023.dravidianlangtech-1.7
%P 52-58
Markdown (Informal)
[Findings of the Second Shared Task on Offensive Span Identification from Code-Mixed Tamil-English Comments](https://aclanthology.org/2023.dravidianlangtech-1.7) (Ravikiran et al., DravidianLangTech-WS 2023)
ACL