Dense Retrieval for Low Resource Languages -- the Case of Amharic Language
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 24 Mar 2025.
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11 of 21 references.
References · 11
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
- 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 464
- 2020A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languagescited by 131
- 2022From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effectivecited by 98
- 2024SPLADE-v3: New baselines for SPLADEcited by 69
- 2020Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasetscited by 12
- 2024Training on the Test Model: Contamination in Ranking Distillationcited by 1
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Citing papers per year: 2026: 3.
3 papers.