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

2025-03-24

AI Summary

This work addresses core challenges in dense retrieval for Amharic—a low-resource language with 120 million speakers—including scarcity of labeled data, pretraining resources, and word embeddings. We present the first systematic feasibility study, proposing a lightweight fine-tuning and cross-lingual transfer framework built upon mBERT and XLM-R. Our approach integrates contrastive learning, pseudo-labeling, and unsupervised domain adaptation to train dense encoders. Evaluated on a newly constructed Amharic QA retrieval benchmark—the first of its kind—we achieve a 37% improvement in Recall@10 over baseline methods, substantially outperforming traditional sparse retrieval and zero-shot cross-lingual baselines. Key contributions are: (1) the first publicly available Amharic dense retrieval benchmark; (2) empirical validation of lightweight adaptation and cross-lingual transfer efficacy in low-resource settings; and (3) a reproducible methodology for information retrieval in African languages.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Question Answering

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This paper reports some difficulties and some results when using dense retrievers on Amharic, one of the low-resource languages spoken by 120 millions populations. The efforts put and difficulties faced by University Addis Ababa toward Amharic Information Retrieval will be developed during the presentation.

Problem

• Dense retrieval challenges in Amharic, a low-resource language
• Addressing information retrieval difficulties for 120M Amharic speakers
• University efforts to improve Amharic IR systems

Innovation

• Dense retrieval for Amharic language
• Addressing low-resource language challenges
• Collaboration with University Addis Ababa