@inproceedings{hande-etal-2022-best,
title = "The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced {K}annada",
author = "Hande, Adeep and
U Hegde, Siddhanth and
S, Sangeetha and
Priyadharshini, Ruba and
Chakravarthi, Bharathi Raja",
editor = "Chakravarthi, Bharathi Raja and
Bharathi, B and
McCrae, John P and
Zarrouk, Manel and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ltedi-1.14",
doi = "10.18653/v1/2022.ltedi-1.14",
pages = "127--135",
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.",
}
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%0 Conference Proceedings
%T The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada
%A Hande, Adeep
%A U Hegde, Siddhanth
%A S, Sangeetha
%A Priyadharshini, Ruba
%A Chakravarthi, Bharathi Raja
%Y Chakravarthi, Bharathi Raja
%Y Bharathi, B.
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F hande-etal-2022-best
%X 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.
%R 10.18653/v1/2022.ltedi-1.14
%U https://aclanthology.org/2022.ltedi-1.14
%U https://doi.org/10.18653/v1/2022.ltedi-1.14
%P 127-135
Markdown (Informal)
[The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada](https://aclanthology.org/2022.ltedi-1.14) (Hande et al., LTEDI 2022)
ACL