@inproceedings{ravikiran-etal-2024-findings,
title = "Findings of the First Shared Task on Offensive Span Identification from Code-Mixed {K}annada-{E}nglish Comments",
author = "Ravikiran, Manikandan and
Rajalakshmi, Ratnavel and
Chakravarthi, Bharathi Raja and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Nadarajan, Rajeswari and
Ravikiran, Manikandan",
booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.dravidianlangtech-1.7",
pages = "43--48",
abstract = "Effectively managing offensive content is crucial on social media platforms to encourage positive online interactions. However, addressing offensive contents in code-mixed Dravidian languages faces challenges, as current moderation methods focus on flagging entire comments rather than pinpointing specific offensive segments. This limitation stems from a lack of annotated data and accessible systems designed to identify offensive language sections. To address this, our shared task presents a dataset comprising Kannada-English code-mixed social comments, encompassing offensive comments. This paper outlines the dataset, the utilized algorithms, and the results obtained by systems participating in this shared task.",
}
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%0 Conference Proceedings
%T Findings of the First Shared Task on Offensive Span Identification from Code-Mixed Kannada-English Comments
%A Ravikiran, Manikandan
%A Rajalakshmi, Ratnavel
%A Chakravarthi, Bharathi Raja
%A Madasamy, Anand Kumar
%A Thavareesan, Sajeetha
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Nadarajan, Rajeswari
%Y Ravikiran, Manikandan
%S Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F ravikiran-etal-2024-findings
%X Effectively managing offensive content is crucial on social media platforms to encourage positive online interactions. However, addressing offensive contents in code-mixed Dravidian languages faces challenges, as current moderation methods focus on flagging entire comments rather than pinpointing specific offensive segments. This limitation stems from a lack of annotated data and accessible systems designed to identify offensive language sections. To address this, our shared task presents a dataset comprising Kannada-English code-mixed social comments, encompassing offensive comments. This paper outlines the dataset, the utilized algorithms, and the results obtained by systems participating in this shared task.
%U https://aclanthology.org/2024.dravidianlangtech-1.7
%P 43-48
Markdown (Informal)
[Findings of the First Shared Task on Offensive Span Identification from Code-Mixed Kannada-English Comments](https://aclanthology.org/2024.dravidianlangtech-1.7) (Ravikiran et al., DravidianLangTech-WS 2024)
ACL