Mohand Boughanem — accepted AI-conference papers

2024

Explanation Extraction from Hierarchical Classification Frameworks for Long Legal Documents

Nishchal Prasad, Taoufiq Dkaki, Mohand Boughanem

NAACL 2024findings

Hierarchical classification frameworks have been widely used to process long sequences, especially in the legal domain for predictions from long legal documents. But being black-box models they are unable to explain their predictions making them less reliable for practical applications, more so in t…

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2024

Rebalancing Label Distribution While Eliminating Inherent Waiting Time in Multi Label Active Learning Applied to Transformers

Maxime Arens, Lucile Callebert, Mohand Boughanem, Jose G. Moreno

COLING 2024main

Data annotation is crucial for machine learning, notably in technical domains, where the quality and quantity of annotated data, significantly affect effectiveness of trained models. Employing humans is costly, especially when annotating for multi-label classification, as instances may bear multiple…

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