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Christian Khairallah


2024

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Computational Morphology and Lexicography Modeling of Modern Standard Arabic Nominals
Christian Khairallah | Reham Marzouk | Salam Khalifa | Mayar Nassar | Nizar Habash
Findings of the Association for Computational Linguistics: EACL 2024

Modern Standard Arabic (MSA) nominals present many morphological and lexical modeling challenges that have not been consistently addressed previously. This paper attempts to define the space of such challenges, and leverage a recently proposed morphological framework to build a comprehensive and extensible model for MSA nominals. Our model design addresses the nominals’ intricate morphotactics, as well as their paradigmatic irregularities. Our implementation showcases enhanced accuracy and consistency compared to a commonly used MSA morphological analyzer and generator. We make our models publicly available.

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Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic
Christian Khairallah | Salam Khalifa | Reham Marzouk | Mayar Nassar | Nizar Habash
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

We present Camel Morph MSA, the largest open-source Modern Standard Arabic morphological analyzer and generator. Camel Morph MSA has over 100K lemmas, and includes rarely modeled morphological features of Modern Standard Arabic with Classical Arabic origins. Camel Morph MSA can produce ∼1.45B analyses and ∼535M unique diacritizations, almost an order of magnitude larger than SAMA (Maamouri et al., 2010c), in addition to having ∼36% less OOV rate than SAMA on a 10B word corpus. Furthermore, Camel Morph MSA fills the gaps of many lemma paradigms by modeling linguistic phenomena consistently. Camel Morph MSA seamlessly integrates with the Camel Tools Python toolkit (Obeid et al., 2020), ensuring ease of use and accessibility.

2023

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Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation
Bashar Alhafni | Go Inoue | Christian Khairallah | Nizar Habash
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Grammatical error correction (GEC) is a well-explored problem in English with many existing models and datasets. However, research on GEC in morphologically rich languages has been limited due to challenges such as data scarcity and language complexity. In this paper, we present the first results on Arabic GEC using two newly developed Transformer-based pretrained sequence-to-sequence models. We also define the task of multi-class Arabic grammatical error detection (GED) and present the first results on multi-class Arabic GED. We show that using GED information as auxiliary input in GEC models improves GEC performance across three datasets spanning different genres. Moreover, we also investigate the use of contextual morphological preprocessing in aiding GEC systems. Our models achieve SOTA results on two Arabic GEC shared task datasets and establish a strong benchmark on a recently created dataset. We make our code, data, and pretrained models publicly available.

2022

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Maknuune: A Large Open Palestinian Arabic Lexicon
Shahd Salah Uddin Dibas | Christian Khairallah | Nizar Habash | Omar Fayez Sadi | Tariq Sairafy | Karmel Sarabta | Abrar Ardah
Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)

We present Maknuune, a large open lexicon for the Palestinian Arabic dialect. Maknuune has over 36K entries from 17K lemmas, and 3.7K roots. All entries include diacritized Arabic orthography, phonological transcription and English glosses. Some entries are enriched with additional information such as broken plurals and templatic feminine forms, associated phrases and collocations, Standard Arabic glosses, and examples or notes on grammar, usage, or location of collected entry

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Morphotactic Modeling in an Open-source Multi-dialectal Arabic Morphological Analyzer and Generator
Nizar Habash | Reham Marzouk | Christian Khairallah | Salam Khalifa
Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

Arabic is a morphologically rich and complex language, with numerous dialectal variants. Previous efforts on Arabic morphology modeling focused on specific variants and specific domains using a range of techniques with different degrees of linguistic modeling transparency. In this paper we propose a new approach to modeling Arabic morphology with an eye towards multi-dialectness, resource openness, and easy extensibility and use. We demonstrate our approach by modeling verbs from Standard Arabic and Egyptian Arabic, within a common framework, and with high coverage.