Eylon Gueta
Eylon Gueta wrote 2 of the papers Mova reads, in 2022–2023, most of them on Named entity recognition; often with Reut Tsarfaty, Amit Seker, Avi Shmidman.
Topics: Named entity recognition 1
Papers
- Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew (2023)
The question is whether a BERT-style Hebrew model learns better when its tokenizer is told about Hebrew morphology before pre-training, instead of a purely statistical WordPiece split. - Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All (2022)
The authors train AlephBERTGimmel, a Modern Hebrew BERT with a 128K word-piece vocabulary (earlier models had 2.4K–52K), on the same ~2 billion words as AlephBERT.
Co-authors (9)
- Reut Tsarfaty ×2
- Amit Seker
- Avi Shmidman
- Cheyn Shmuel Shmidman
- Dan Bareket
- Joshua Guedalia
- Moshe Koppel
- Omer Goldman
- Shaltiel Shmidman
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