TY - GEN
T1 - IIITDWD_SVC@DravidianLangTech-2024
T2 - 4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024
AU - Sai, Chava Srinivasa
AU - Kumar, Rangoori Vinay
AU - Saumya, Sunil
AU - Biradar, Shankar
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - Social media platforms have become increasingly popular and are utilized for a wide range of purposes, including product promotion, news sharing, accomplishment sharing, and much more. However, it is also employed for defamatory speech, intimidation, and the propagation of untruths about particular groups of people. Further, hateful and offensive posts spread quickly and often have a negative impact on people; it is important to identify and remove them from social media platforms as soon as possible. Over the past few years, research on hate speech detection and offensive content has grown in popularity. One of the many difficulties in identifying hate speech on social media platforms is the use of code-mixed language. The majority of people who use social media typically share their messages in languages with mixed codes, like Telugu–English. To encourage research in this direction, the organizers of DravidianLangTech@EACL-2024 conducted a shared task to identify hateful content in Telugu-English code-mixed text. Our team participated in this shared task, employing three different models: Xlm-Roberta, BERT, and Hate-BERT. In particular, our BERT-based model secured the 14th rank in the competition with a macro F1 score of 0.65.
AB - Social media platforms have become increasingly popular and are utilized for a wide range of purposes, including product promotion, news sharing, accomplishment sharing, and much more. However, it is also employed for defamatory speech, intimidation, and the propagation of untruths about particular groups of people. Further, hateful and offensive posts spread quickly and often have a negative impact on people; it is important to identify and remove them from social media platforms as soon as possible. Over the past few years, research on hate speech detection and offensive content has grown in popularity. One of the many difficulties in identifying hate speech on social media platforms is the use of code-mixed language. The majority of people who use social media typically share their messages in languages with mixed codes, like Telugu–English. To encourage research in this direction, the organizers of DravidianLangTech@EACL-2024 conducted a shared task to identify hateful content in Telugu-English code-mixed text. Our team participated in this shared task, employing three different models: Xlm-Roberta, BERT, and Hate-BERT. In particular, our BERT-based model secured the 14th rank in the competition with a macro F1 score of 0.65.
UR - https://www.scopus.com/pages/publications/85189863891
UR - https://www.scopus.com/pages/publications/85189863891#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:85189863891
T3 - DravidianLangTech 2024 - 4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, Proceedings of the Workshop
SP - 119
EP - 123
BT - DravidianLangTech 2024 - 4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, Proceedings of the Workshop
A2 - Chakravarthi, Bharathi Raja
A2 - Priyadharshini, Ruba
A2 - Madasamy, Anand Kumar
A2 - Thavareesan, Sajeetha
A2 - Sherly, Elizabeth
A2 - Nadarajan, Rajeswari
A2 - Ravikiran, Manikandan
PB - Association for Computational Linguistics (ACL)
Y2 - 22 March 2024
ER -