MiqraBERT, a finetuned Sentence-BERT model, achieves 2.7-fold better distributional separation of parallel versus non-parallel Biblical Hebrew verses and reduces ambiguous overlap from 24% to 6%, with strong performance on narrative but weak on poetic parallels.
arXiv preprint arXiv:2308.16687 , year=
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AI models for automated short answer scoring show substantial mid-range quality degradation in expert agreement that improves with greater task-specific adaptation.
citing papers explorer
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MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection
MiqraBERT, a finetuned Sentence-BERT model, achieves 2.7-fold better distributional separation of parallel versus non-parallel Biblical Hebrew verses and reduces ambiguous overlap from 24% to 6%, with strong performance on narrative but weak on poetic parallels.
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Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation
AI models for automated short answer scoring show substantial mid-range quality degradation in expert agreement that improves with greater task-specific adaptation.