@inproceedings{moni-etal-2026-team,
    title = "Team Oryu@{D}ravidian{L}ang{T}ech 2026: A Multilingual Transformer Approach for Hope Speech Detection in Code-Mixed {T}ulu",
    author = "Moni, Joyeta Barua  and
      Orny, Noore Tamanna  and
      Kabir, Md. Abtahee  and
      Murad, Hasan",
    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-1.64/",
    pages = "403--408",
    ISBN = "979-8-89176-401-9",
    abstract = "Hope speech detection appears to have an essential role to play in fostering positive and inclusive communication on social media, especially in low-resource multilingual settings. This paper describes the system submitted by Team Oryu for Task 1: Coarse-Grained Hope Tone Classification in Code-Mixed Tulu. The task involves classifying comments in social media texts into one of the four classes: Encouraging, Discouraging, Uninvolved, and Blended Tone. The texts in this task show heavy code-mixing between Tulu, English, and Kannada. In order to overcome this challenge, we employed a fine-tuned multilingual transformer model, code-mixed text processing, data augmentation, and class-weighted loss to handle class imbalance. Our proposed system achieved a Macro F1-score of 63{\%}, securing 3rd position on the shared task. The results demonstrate the efficacy of multilingual transformer models in emotionally nuanced classification in code-mixed environments while underscoring the difficulties in capturing blended emotional tones."
}