3 citations · 5 across the 6 of their papers we have counts for
6 papers
CoastTerm: a Corpus for Multidisciplinary Term Extraction in Coastal Scientific Literature
Julien Delaunay, Hanh Thi Hong Tran, Carlos-Emiliano González-Gallardo +6
The growing impact of climate change on coastal areas, particularly active but fragile regions, necessitates collaboration among diverse stakeholders and disciplines to formulate e…
Does It Make Sense to Explain a Black Box With Another Black Box?
Julien Delaunay, Luis Galárraga, Christine Largouët
Although counterfactual explanations are a popular approach to explain ML black-box classifiers, they are less widespread in NLP. Most methods find those explanations by iterativel…
Explainability for Machine Learning Models: From Data Adaptability to User Perception
julien Delaunay
This thesis explores the generation of local explanations for already deployed machine learning models, aiming to identify optimal conditions for producing meaningful explanations…
"Honey, Tell Me What's Wrong", Global Explanation of Textual Discriminative Models through Cooperative Generation
Antoine Chaffin, Julien Delaunay
The ubiquity of complex machine learning has raised the importance of model-agnostic explanation algorithms. These methods create artificial instances by slightly perturbing real i…
A Comprehensive Survey of Document-level Relation Extraction (2016-2023)
Julien Delaunay, Hanh Thi Hong Tran, Carlos-Emiliano González-Gallardo +3
Document-level relation extraction (DocRE) is an active area of research in natural language processing (NLP) concerned with identifying and extracting relationships between entiti…
s-LIME: Reconciling Locality and Fidelity in Linear Explanations
Romaric Gaudel, Luis Galárraga, Julien Delaunay +2
The benefit of locality is one of the major premises of LIME, one of the most prominent methods to explain black-box machine learning models. This emphasis relies on the postulate…