Work

Team Oryu@DravidianLangTech 2026: A Multilingual Transformer Approach for Hope Speech Detection in Code-Mixed Tulu

Year: 2026

Type: conference-paper

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 F1score 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.

Source: Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangTech)

DOI: 10.18653/v1/2026.dravidianlangtech-1.64

Authors: Joyeta Barua Moni, Noore Tamanna Orny, Md. Abtahee Kabir, Hasan Murad

Institution: Chittagong University of Engineering & Technology

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7166801272

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