The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada

Adeep Hande, Siddhanth U Hegde, Sangeetha S, Ruba Priyadharshini, Bharathi Raja Chakravarthi


Abstract
In recent years, various methods have been developed to control the spread of negativity by removing profane, aggressive, and offensive comments from social media platforms. There is, however, a scarcity of research focusing on embracing positivity and reinforcing supportive and reassuring content in online forums. As a result, we concentrate our research on developing systems to detect hope speech in code-mixed Kannada. As a result, we present DC-LM, a dual-channel language model that sees hope speech by using the English translations of the code-mixed dataset for additional training. The approach is jointly modelled on both English and code-mixed Kannada to enable effective cross-lingual transfer between the languages. With a weighted F1-score of 0.756, the method outperforms other models. We aim to initiate research in Kannada while encouraging researchers to take a pragmatic approach to inspire positive and supportive online content.
Anthology ID:
2022.ltedi-1.14
Volume:
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | LTEDI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
127–135
Language:
URL:
https://aclanthology.org/2022.ltedi-1.14
DOI:
10.18653/v1/2022.ltedi-1.14
Bibkey:
Cite (ACL):
Adeep Hande, Siddhanth U Hegde, Sangeetha S, Ruba Priyadharshini, and Bharathi Raja Chakravarthi. 2022. The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 127–135, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada (Hande et al., LTEDI 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.ltedi-1.14.pdf
Data
KanHope