3 citations · 6 across the 8 of their papers we have counts for
8 papers
Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLM
Everlyn Asiko Chimoto, Mostafa Elhoushi, Bruce A. Bassett
Quantization is an effective technique for reducing the storage footprint and computational costs of Large Language Models (LLMs), but it often results in performance degradation.…
The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages
Jenalea Rajab, Anuoluwapo Aremu, Everlyn Asiko Chimoto +12
This paper presents the Esethu Framework, a sustainable data curation framework specifically designed to empower local communities and ensure equitable benefit-sharing from their l…
State of NLP in Kenya: A Survey
Cynthia Jayne Amol, Everlyn Asiko Chimoto, Rose Delilah Gesicho +13
Kenya, known for its linguistic diversity, faces unique challenges and promising opportunities in advancing Natural Language Processing (NLP) technologies, particularly for its und…
GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learning
Rita Ramos, Everlyn Asiko Chimoto, Maartje ter Hoeve +1
We introduce GrammaMT, a grammatically-aware prompting approach for machine translation that uses Interlinear Glossed Text (IGT), a common form of linguistic description providing…
Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning
Everlyn Asiko Chimoto, Jay Gala, Orevaoghene Ahia +3
Neural Machine Translation models are extremely data and compute-hungry. However, not all data points contribute equally to model training and generalization. Data pruning to remov…
Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili
Christiaan Jacobs, Nathanaël Carraz Rakotonirina, Everlyn Asiko Chimoto +2
We consider hate speech detection through keyword spotting on radio broadcasts. One approach is to build an automatic speech recognition (ASR) system for the target low-resource la…
Very Low Resource Sentence Alignment: Luhya and Swahili
Everlyn Asiko Chimoto, Bruce A. Bassett
Language-agnostic sentence embeddings generated by pre-trained models such as LASER and LaBSE are attractive options for mining large datasets to produce parallel corpora for low-r…
COMET-QE and Active Learning for Low-Resource Machine Translation
Everlyn Asiko Chimoto, Bruce A. Bassett
Active learning aims to deliver maximum benefit when resources are scarce. We use COMET-QE, a reference-free evaluation metric, to select sentences for low-resource neural machine…