Dense Retrieval for Low Resource Languages -- the Case of Amharic Language

This paper presents our investigation into dense retrieval models for Amharic, a low-resource language spoken by more than 120 million people. We constructed training datasets tailored to dense retrieval models and evaluated model performance by comparing dense and sparse retrieval approaches on Amharic information retrieval. The study also highlights the challenges and efforts involved in advancing retrieval systems for low-resource languages.