Papers by Andrei Voinea

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Challenge:	Pre-trained multilingual models lack exposure to low-resource ancient languages such as Sumerian.
Approach:	They build a hierarchical validator capturing subword, word, and part-of-speech patterns from 4,545 annotated sequences and use it to filter candidates and identify hard negatives for fine-tuning.
Outcome:	The proposed model achieves 18.0% hit@10 accuracy, while the validator alone improves this to 72.8%, while hard negative fine-tuning reaches 78.3%.