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E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models
Enhancing the interpretability of text classification models can help increase the reliability of these models in real-world applications.
DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning
Secondly, we propose a class-aware parallel adaptation method that aligns class-level distributions for each source-target language pair, thereby alleviating the language pairs' language gap.
ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition
Typically, these approaches adopt a teacher-student architecture, where the teacher network is trained in the source language, and the student network seeks to learn knowledge from the teacher network and is expected to perform well in the target language.
A Segmentation-Oriented Inter-Class Transfer Method: Application to Retinal Vessel Segmentation
Next, the semi-supervised clustering is conducted to select instances, from different sources databases, possessing similarities in the feature space.
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