6 papers
Improving Neural Retrieval with Attribution-Guided Query Rewriting
Moncef Garouani, Josiane Mothe
Neural retrievers are effective but brittle: underspecified or ambiguous queries can misdirect ranking even when relevant documents exist. Existing approaches address this brittlen…
AmharicIR+Instr: A Two-Dataset Resource for Neural Retrieval and Instruction Tuning
Tilahun Yeshambel, Moncef Garouani, Josiane Mothe
Neural retrieval and GPT-style generative models rely on large, high-quality supervised data, which is still scarce for low-resource languages such as Amharic. We release an Amhari…
AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials
Mohammad El Sakka, Caroline De Pourtales, Lotfi Chaari +1
Remote sensing has emerged as a critical tool for large-scale Earth monitoring and land management. In this paper, we introduce AgriPotential, a novel benchmark dataset composed of…
GeMix: Conditional GAN-Based Mixup for Improved Medical Image Augmentation
Hugo Carlesso, Maria Eliza Patulea, Moncef Garouani +2
Mixup has become a popular augmentation strategy for image classification, yet its naive pixel-wise interpolation often produces unrealistic images that can hinder learning, partic…
Investigating the Duality of Interpretability and Explainability in Machine Learning
Moncef Garouani, Josiane Mothe, Ayah Barhrhouj +1
The rapid evolution of machine learning (ML) has led to the widespread adoption of complex "black box" models, such as deep neural networks and ensemble methods. These models exhib…
Dense Retrieval for Low Resource Languages -- the Case of Amharic Language
Tilahun Yeshambel, Moncef Garouani, Serge Molina +1
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…