Christian Khairallah


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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.


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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 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.