This paper utilized a pre-trained DeBERTa-V3 backbone to capture semantic meaning through disentangled attention to capture semantic meaning through disentangled attention, and proposed a Hybrid MSE-CCC Loss to identify distributional relationships that simple regression missed.
This work examines the effectiveness of stacking traditional machine learning techniques like Logistic Regression, k-Nearest Neighbors, Random Forest, Multi-Layer Perceptron, Support Vector Classifier and simple word counting and compares them to BERT, a powerful pre-trained language model.
Björn Kießwetter, Christoph Lukas, Tim Sieber· 0 citations
The integration of the IndoBERT-BiLSTM architecture with SHAP is demonstrated to deliver accurate and explainable Indonesian sentiment analysis, which effectively bridges the gap between deep learning performance and decision transparency without compromising classification accuracy.
A. Widiyatmoko, A. Nugroho, Muhammad Nurul Firdaus· Journal of Electrical Engine...· 0 citations
A unified evaluation framework testing gradient and loss-based balancing strategies under controlled settings, and a theoretical diagnosis explaining why these methods fail, as they conflate fitting speed with discriminative contribution are provided.
Ioanna Kaffeza, Efthymios Georgiou, A. Potamianos· 0 citations
Results show systematic variation across domains: technical and methodological areas such as deep learning and natural language processing exhibit gain-salient framing, while safety-critical topics such as deepfakes and facial recognition show strongly loss-salient profiles.
O. Topal, Inna Novalija, Joao Pita Costa et al.· Applied Informatics· 0 citations
Online company review platforms have gained significant importance as they provide transparent insights into corporate culture and employee satisfaction. However, analyzing this feedback at scale remains challenging due to the linguistic complexity of workplace-specific discourse and the lack of high-quality, multi-dim...
Khanh-Long Ho-Vuong, Nhat-Huy Dang, Do Bao et al.· International Conference on...· 0 citations
A model-agnostic metric, the EPC score, which is an extension of the Explainability-Performance Coefficient (EPC), that quantifies explanation quality by explicitly balancing the trade-off between feature selection sparsity and preserved model performance is proposed.
Christian Oliva, Luis Fernando Lago-Fernández· arXiv.org· 0 citations
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