aakanshajagga14/rl-ethical-mt-low-resource

# Ethical Reinforcement Learning for Machine Translation in Low-Resource Languages This repository accompanies the research paper: **"Integrating Sparse Reward Handling, Ethical Considerations, and Domain-Specific Adaptation in RL-Based Machine Translation for Low-Resource Languages"** *Author: Aakansha Jagga* ## 🔍 Overview Machine translation for low-resource languages suffers from sparse feedback, bias, and lack of domain adaptability. This project proposes a holistic reinforcement learning framework that integrates: - Sparse reward handling techniques - Ethical AI principles (fairness, bias mitigation, cultural sensitivity) - Domain-specific adaptation strategies ## 🚀 Key Contributions - Reward shaping & self-critical sequence training for sparse RL signals - Ethical evaluation pipelines for fairness and cultural bias - Domain-aware adaptation for technical & colloquial language use ## 🏗 Architecture - Base Model: Transformer-based NMT - Training Paradigm: Reinforcement Learning with sparse rewards - Evaluation: BLEU, linguistic coherence, and human feedback ## 📊 Evaluation Metrics - BLEU Score - Linguistic Coherence - Ethical Fairness Metrics - User Feedback from native speakers ## 📁 Repository Guide - `src/` – Model, training, evaluation, and ethics modules - `experiments/` – Ablation and analysis - `paper/` – Published research paper - `results/` – Quantitative & qualitative outputs ## 📜 Citation If you use this work, please cite: ```bibtex @article{jagga2024ethicalrlmt, title={Integrating Sparse Reward Handling, Ethical Considerations, and Domain-Specific Adaptation in RL-Based Machine Translation for Low-Resource Languages}, author={Jagga, Aakansha}, journal={IOSR Journal of Computer Engineering}, year={2024} }