Aug 22, 2023 · We introduce in this paper a new approach to improve deep learning-based architectures for multi-label document classification.
We introduce in this paper a new approach to improve deep learningbased architectures for multi-label document classification. Dependencies between labels ...
We proposed in this paper an effective way of using the pairwise label co-occurrence information to allow transformers to learn dependencies between labels.
Datasets and notebooks for Exploiting Label Dependencies for Multi-Label Document Classification Using Transformers
In this work we propose the Classification Transformer (C-Tran), a general framework for multi-label image classification that leverages Transformers to exploit ...
We propose a transformer-based model for multi-label image classification that exploits dependencies among a target set of labels using an encoder transformer.
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RÉSUMÉ. Dans cet article, nous proposons des approches pour améliorer les architectures basées sur des transformeurs pour la classification de documents ...
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We measure the pair-wise dependency between labels in the four datasets included in this study, using Fisher's exact test.
Jul 7, 2022 · These methods manage to capture the semantic features of the document but fail to consider the dependencies that can exist between labels.
Apr 18, 2024 · So sure, for a classification (or multi label classification) of text, Transformers are good. I would take a pre-trained Bert flavoured ...
Missing: Exploiting Dependencies