30 citations · 93 across the 15 of their papers we have counts for
16 papers
Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?
Avi Shmidman, Cheyn Shmuel Shmidman, Dan Bareket +2
Semitic morphologically-rich languages (MRLs) are characterized by extreme word ambiguity. Because most vowels are omitted in standard texts, many of the words are homographs with…
Introducing DictaLM -- A Large Generative Language Model for Modern Hebrew
Shaltiel Shmidman, Avi Shmidman, Amir David Nissan Cohen +1
We present DictaLM, a large-scale language model tailored for Modern Hebrew. Boasting 7B parameters, this model is predominantly trained on Hebrew-centric data. As a commitment to…
DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew
Shaltiel Shmidman, Avi Shmidman, Moshe Koppel
We present DictaBERT, a new state-of-the-art pre-trained BERT model for modern Hebrew, outperforming existing models on most benchmarks. Additionally, we release three fine-tuned v…
Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All
Eylon Gueta, Avi Shmidman, Shaltiel Shmidman +6
We present a new pre-trained language model (PLM) for modern Hebrew, termed AlephBERTGimmel, which employs a much larger vocabulary (128K items) than standard Hebrew PLMs before. W…
Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language
Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman +3
We present a new pre-trained language model (PLM) for Rabbinic Hebrew, termed Berel (BERT Embeddings for Rabbinic-Encoded Language). Whilst other PLMs exist for processing Hebrew t…
A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration
Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman +2
One of the primary tasks of morphological parsers is the disambiguation of homographs. Particularly difficult are cases of unbalanced ambiguity, where one of the possible analyses…