COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks

Abstract

Transformer architectures are complex and their use in NLP, while it has engendered many successes, makes their interpretability or explainability challenging. Recent debates have shown that attention maps and attribution methods are unreliable (Pruthi et al., 2019; Brunner et al., 2019). In this paper, we present some of their limitations and introduce COCKATIEL, which successfully addresses some of them. COCKATIEL is a novel, post-hoc, conceptbased, model-agnostic XAI technique that generates meaningful explanations from the last layer of a neural net model trained on an NLP classification task by using Non-Negative Matrix Factorization (NMF) to discover the concepts the model leverages to make predictions and by exploiting a Sensitivity Analysis to estimate accurately the importance of each of these concepts for the model. It does so without compromising the accuracy of the underlying model or requiring a new one to be trained. We conduct experiments in single and multiaspect sentiment analysis tasks and we show COCKATIEL's superior ability to discover concepts that align with humans' on Transformer models without any supervision, we objectively verify the faithfulness of its explanations through fidelity metrics, and we showcase its ability to provide meaningful explanations in two different datasets.
Fichier principal
Vignette du fichier
2023.findings-acl.317.pdf (7.43 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-04223218 , version 1 (29-09-2023)

Identifiers

Cite

Fanny Jourdan, Agustin Picard, Thomas Fel, Laurent Risser, Jean-Michel Loubes, et al.. COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks. 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023), Jul 2023, Toronto, Canada. pp.5120-5136. ⟨hal-04223218⟩
58 View
12 Download

Altmetric

Share

Gmail Facebook X LinkedIn More