Christian Khairallah — accepted AI-conference papers

2024

Camel Morph MSA: A Large-Scale Open-Source Morphological Analyzer for Modern Standard Arabic

Christian Khairallah, Salam Khalifa, Reham Marzouk, Mayar Nassar, Nizar Habash

COLING 2024main

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 analys…

Cited by 2 Source ↗PDF ↗Scholar ↗

2023

Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation

Bashar Alhafni, Go Inoue, Christian Khairallah, Nizar Habash

EMNLP 2023long main

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…

Cited by 0 Source ↗code ↗Scholar ↗