@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Team Oryu@DravidianLangTech 2026: A Multilingual Transformer Approach for Hope Speech Detection in Code-Mixed Tulu
%A Moni, Joyeta Barua
%A Orny, Noore Tamanna
%A Kabir, Md. Abtahee
%A Murad, Hasan
%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 moni-etal-2026-team
%X 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.
%U https://aclanthology.org/2026.dravidianlangtech-1.64/
%P 403-408
Markdown (Informal)
[Team Oryu@DravidianLangTech 2026: A Multilingual Transformer Approach for Hope Speech Detection in Code-Mixed Tulu](https://aclanthology.org/2026.dravidianlangtech-1.64/) (Moni et al., DravidianLangTech 2026)
ACL