The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada
Adeep Hande, Siddhanth U Hegde, S Sangeetha, Ruba Priyadharshini, Bharathi Raja Chakravarthi · 2022
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.