Papers by Moshe Koppel
Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses? (2023.eacl-main)
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| Challenge: | Semitic morphologically-rich languages are characterized by extreme word ambiguity . many of the words are homographs with multiple possible analyses . |
| Approach: | They evaluate existing models for Hebrew homographs using word-piece embeddings . they find they are more effective when the number of word-part splits is limited . |
| Outcome: | The proposed models outperform non-contextualized embeddings on Hebrew homograph challenge sets. |
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. |
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. |