Papers by Christian Khairallah
Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic (2024.lrec-main)
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| Challenge: | Camel Morph MSA is the largest open-source Modern Standard Arabic morphological analyzer and generator. |
| Approach: | They present Camel Morph MSA, the largest open-source Arabic morphological analyzer and generator. |
| Outcome: | The analysis can produce 1.45B analyses and 535M unique diacritizations, almost an order of magnitude larger than SAMA on a 10B word corpus. |
Computational Morphology and Lexicography Modeling of Modern Standard Arabic Nominals (2024.findings-eacl)
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| Challenge: | Modern Standard Arabic (MSA) nominals present many morphological and lexical modeling challenges that have not been consistently addressed before. |
| Approach: | They propose to use a morphological framework to model Arabic nominals using a proposed morphology framework. |
| Outcome: | The proposed model improves accuracy and consistency compared to a commonly used morphological analyzer and generator. |
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation (2023.emnlp-main)
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| Challenge: | Existing studies on grammatical error correction (GEC) in morphologically rich languages have been limited due to data scarcity and language complexity. |
| Approach: | They propose to use Arabic GEC to improve performance across three datasets . they define Arabic grammatical error detection task as auxiliary input . |
| Outcome: | The proposed models achieve SOTA results on two Arabic GEC shared task datasets and establish a strong benchmark on a recently created dataset. |