SSN-Nova@LT-EDI 2024: POS Tagging, Boosting Techniques and Voting Classifiers for Caste And Migration Hate Speech Detection

A Reddy, Ann Thomas, Pranav Moorthi, Bharathi B


Abstract
This paper presents our submission for the shared task on Caste and Migration Hate Speech Detection: LT-EDI@EACL 20241 . This text classification task aims to foster the creation of models capable of identifying hate speech related to caste and migration. The dataset comprises social media comments, and the goal is to categorize them into negative and positive sentiments. Our approach explores back-translation for data augmentation to address sparse datasets in low-resource Dravidian languages. While Part-of-Speech (POS) tagging is valuable in natural language processing, our work highlights its ineffectiveness in Dravidian languages, with model performance drastically reducing from 0.73 to 0.67 on application. In analyzing boosting and ensemble methods, the voting classifier with traditional models outperforms others and the boosting techniques, underscoring the efficacy of simper models on low-resource data despite augmentation.
Anthology ID:
2024.ltedi-1.29
Volume:
Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
Month:
March
Year:
2024
Address:
St. Julian's, Malta
Editors:
Bharathi Raja Chakravarthi, Bharathi B, Paul Buitelaar, Thenmozhi Durairaj, György Kovács, Miguel Ángel García Cumbreras
Venues:
LTEDI | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
233–237
Language:
URL:
https://aclanthology.org/2024.ltedi-1.29
DOI:
Bibkey:
Cite (ACL):
A Reddy, Ann Thomas, Pranav Moorthi, and Bharathi B. 2024. SSN-Nova@LT-EDI 2024: POS Tagging, Boosting Techniques and Voting Classifiers for Caste And Migration Hate Speech Detection. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 233–237, St. Julian's, Malta. Association for Computational Linguistics.
Cite (Informal):
SSN-Nova@LT-EDI 2024: POS Tagging, Boosting Techniques and Voting Classifiers for Caste And Migration Hate Speech Detection (Reddy et al., LTEDI-WS 2024)
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