@inproceedings{a-etal-2026-ssn,
title = "{SSN}{\_}{H}ope{N}etters@{D}ravidian{L}ang{T}ech 2026: Multi-Level Hope Speech Detection using {XLM}-{R}o{BERT}a",
author = "A, Moogambigai and
B, Bharathi and
S, Nikhil Karthik and
D, Pandiarajan and
Saravanan, Nandhika",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Rajiakodi, Saranya and
Navaneethakrishnan, Subalalitha and
Chinnappa, Dhivya and
Palani, Balasubramanian and
Subramanian, Malliga and
Shanmugavadivel, Kogilavani and
Rajalakshmi, Ratnavel",
booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
month = jul,
year = "2026",
address = "Underline (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.dravidianlangtech-1.57/",
pages = "366--370",
ISBN = "979-8-89176-401-9",
abstract = "This paper presents our system submission to the Shared Task on Hope Speech Detection in Code-Mixed Tulu Language at DravidianLangTech @ ACL 2026. We introduce a transformer-based approach built on XLM RoBERTa-base for multilingual hope speechclassification. Our system addresses two sub tasks: coarse-grained classification of hope versus non-hope speech and fine-grained categorization of different hope expressions. Since hope is often expressed in subtle ways, especially in mixed-language text, our model looks at the full context of a sentence to understand its real meaning rather than just focusing on specific words. Experimental results demonstrate that multilingual transformer models effectively model supportive and encouraging expressions, underscoring their suitability for promoting constructive discourse in low-resourceand code-mixed language settings."
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<abstract>This paper presents our system submission to the Shared Task on Hope Speech Detection in Code-Mixed Tulu Language at DravidianLangTech @ ACL 2026. We introduce a transformer-based approach built on XLM RoBERTa-base for multilingual hope speechclassification. Our system addresses two sub tasks: coarse-grained classification of hope versus non-hope speech and fine-grained categorization of different hope expressions. Since hope is often expressed in subtle ways, especially in mixed-language text, our model looks at the full context of a sentence to understand its real meaning rather than just focusing on specific words. Experimental results demonstrate that multilingual transformer models effectively model supportive and encouraging expressions, underscoring their suitability for promoting constructive discourse in low-resourceand code-mixed language settings.</abstract>
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%0 Conference Proceedings
%T SSN_HopeNetters@DravidianLangTech 2026: Multi-Level Hope Speech Detection using XLM-RoBERTa
%A A, Moogambigai
%A B, Bharathi
%A S, Nikhil Karthik
%A D, Pandiarajan
%A Saravanan, Nandhika
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Rajiakodi, Saranya
%Y Navaneethakrishnan, Subalalitha
%Y Chinnappa, Dhivya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Shanmugavadivel, Kogilavani
%Y Rajalakshmi, Ratnavel
%S Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2026
%8 July
%I Association for Computational Linguistics
%C Underline (Virtual)
%@ 979-8-89176-401-9
%F a-etal-2026-ssn
%X This paper presents our system submission to the Shared Task on Hope Speech Detection in Code-Mixed Tulu Language at DravidianLangTech @ ACL 2026. We introduce a transformer-based approach built on XLM RoBERTa-base for multilingual hope speechclassification. Our system addresses two sub tasks: coarse-grained classification of hope versus non-hope speech and fine-grained categorization of different hope expressions. Since hope is often expressed in subtle ways, especially in mixed-language text, our model looks at the full context of a sentence to understand its real meaning rather than just focusing on specific words. Experimental results demonstrate that multilingual transformer models effectively model supportive and encouraging expressions, underscoring their suitability for promoting constructive discourse in low-resourceand code-mixed language settings.
%U https://aclanthology.org/2026.dravidianlangtech-1.57/
%P 366-370
Markdown (Informal)
[SSN_HopeNetters@DravidianLangTech 2026: Multi-Level Hope Speech Detection using XLM-RoBERTa](https://aclanthology.org/2026.dravidianlangtech-1.57/) (A et al., DravidianLangTech 2026)
ACL