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Does It Make Sense to Explain a Black Box With Another Black Box?
Most methods find those explanations by iteratively perturbing the target document until it is classified differently by the black box.
Explainability for Machine Learning Models: From Data Adaptability to User Perception
The primary goal is to develop methods for generating explanations for any model while ensuring that these explanations remain faithful to the underlying model and comprehensible to the users.
"Honey, Tell Me What's Wrong", Global Explanation of Textual Discriminative Models through Cooperative Generation
Because it does not rely on initial samples, it allows to generate explanations even when data is absent (e. g., for confidentiality reasons).
A Comprehensive Survey of Document-level Relation Extraction (2016-2023)
Document-level relation extraction (DocRE) is an active area of research in natural language processing (NLP) concerned with identifying and extracting relationships between entities beyond sentence boundaries.
s-LIME: Reconciling Locality and Fidelity in Linear Explanations
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.
REMI: Mining Intuitive Referring Expressions on Knowledge Bases
A referring expression (RE) is a description that identifies a set of instances unambiguously.
