@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://preview.aclanthology.org/ingest-acl-workshops/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."
}