Papers by Shaltiel Shmidman
Nakdan: Professional Hebrew Diacritizer (2020.acl-demos)
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| Challenge: | a system for automatic diacritization of Hebrew Text is available for both casual and expert users. |
| Approach: | They propose a system for automatic diacritization of Hebrew Text . the system combines declarative linguistic knowledge with machine learning models . |
| Outcome: | The proposed system is available for both casual and expert users. |
A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration (2020.findings-emnlp)
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| Challenge: | morphological parsers face a formidable challenge with unbalanced ambiguities in homographs . case of unbalanciated ambiguity is difficult to disambiguate, especially in cases of unbalancing . a new dataset improves the overall average F1 score for Hebrew homograph . |
| Approach: | They propose a challenge set for Hebrew homographs with substantial attestation of each analysis of 21 Hebrew homographies. |
| Outcome: | The proposed set improves the average F1 score for Hebrew homographs by 0.67 . the annotated datasets are made publicly available for further research. |
Splintering Nonconcatenative Languages for Better Tokenization (2025.findings-acl)
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| Challenge: | Subword tokenizers are not effective in nonconcatenative languages such as Hebrew and Arabic, where morphology is encoded in root-template patterns, or Malay and Georgian, where split affixes are common. |
| Approach: | They propose a pre-processing step which rearranges text into a linear form that better represents nonconcatenative morphologies. |
| Outcome: | The proposed method is based on measures evaluating token vocabularies in Hebrew, Arabic, and Malay, and on BERT-architecture models trained for Hebrew. |
MRL Parsing Without Tears: The Case of Hebrew (2024.findings-acl)
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| Challenge: | a new approach to parsing morphologically rich languages (MRLs) is needed to overcome the deficiencies of current approaches. |
| Approach: | They propose a "flipped pipeline" where multiple layers are predicted independently on whole-token basis and then synthesized. |
| Outcome: | The proposed model achieves near-SOTA performance on Hebrew NLP tasks. |