Jafar Razmara
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The paper proposes a low-cost and interpretable fine-tuning extraction strategy for automatic term extraction, demonstrating consistent and balanced performance on the ATE Shared Task.
The paper introduces an interpretable method for distinguishing genuine hate speech from contextually nuanced reclaimed language, achieving robust performance even with severe class imbalance.
Papers
Peacemaker at ATE-IT: Automatic term extraction from Italian text for waste management data using encoder model
The paper proposes a low-cost and interpretable fine-tuning extraction strategy for automatic term extraction, demonstrating consistent and balanced performance on the ATE Shared Task.