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Found 6 papers, 2 papers with code
AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level
First, so far, Hebrew resources for training large language models are not of the same magnitude as their English counterparts.
The Perfect Victim: Computational Analysis of Judicial Attitudes towards Victims of Sexual Violence
Second, we curate a manually annotated dataset for judicial assessments of victim's credibility in the Hebrew language, as well as a model that can extract credibility labels from court cases.
Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All
We perform a contrastive analysis of this model against all previous Hebrew PLMs (mBERT, heBERT, AlephBERT) and assess the effects of larger vocabularies on task performance.
Ranked #1 on
Named Entity Recognition (NER)
on NEMO-Corpus
AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With
Second, there are no accepted tasks and benchmarks to evaluate the progress of Hebrew PLMs on.
Neural Modeling for Named Entities and Morphology (NEMO^2)
Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens.
From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?
It has been exactly a decade since the first establishment of SPMRL, a research initiative unifying multiple research efforts to address the peculiar challenges of Statistical Parsing for Morphologically-Rich Languages (MRLs). Here we reflect on parsing MRLs in that decade, highlight the solutions and lessons learned for the architectural, modeling and lexical challenges in the pre-neural era, and argue that similar challenges re-emerge in neural architectures for MRLs.
