Text editing frames grammatical error correction (GEC) as a sequence tagging problem, where edit tags are assigned to input tokens, and applying these edits results in the corrected text.This approach has gained attention for its efficiency and interpretability.However, while extensively explored for English, text editing remains largely underexplored for morphologically rich languages like Arabic.In this paper, we introduce a text editing approach that derives edit tags directly from data, eliminating the need for language-specific edits.We demonstrate its effectiveness on Arabic, a diglossic and morphologically rich language, and investigate the impact of different edit representations on model performance.Our approach achieves SOTA results on two Arabic GEC benchmarks and performs on par with SOTA on two others.Additionally, our models are over six times faster than existing Arabic GEC systems, making our approach more practical for real-world applications.Finally, we explore ensemble models, demonstrating how combining different models leads to further performance improvements.We make our code, data, and pretrained models publicly available.1 <

We present the SAMER Corpus, the first manually annotated Arabic parallel corpus for text simplification targeting school-aged learners. Our corpus comprises texts of 159K words selected from 15 publicly available Arabic fiction novels most of which were published between 1865 and 1955. Our corpus includes readability level annotations at both the document and word levels, as well as two simplified parallel versions for each text targeting learners at two different readability levels. We describe the corpus selection process, and outline the guidelines we followed to create the annotations and ensure their quality. Our corpus is publicly available to support and encourage research on Arabic text simplification, Arabic automatic readability assessment, and the development of Arabic pedagogical language technologies.

Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni, Bashar Alhafni, Dhruv Kumar. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Grammatical error correction (GEC) is a wellexplored 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 an 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.1

Gender bias in natural language processing (NLP) applications, particularly machine translation, has been receiving increasing attention. Much of the research on this issue has focused on mitigating gender bias in English NLP models and systems. Addressing the problem in poorly resourced, and/or morphologically rich languages has lagged behind, largely due to the lack of datasets and resources. In this paper, we introduce a new corpus for gender identification and rewriting in contexts involving one or two target users (I and/or You) – first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. The corpus has multiple parallel components: four combinations of 1st and 2nd person in feminine and masculine grammatical genders, as well as English, and English to Arabic machine translation output. This corpus expands on Habash et al. (2019)’s Arabic Parallel Gender Corpus (APGC v1.0) by adding second person targets as well as increasing the total number of sentences over 6.5 times, reaching over 590K words. Our new dataset will aid the research and development of gender identification, controlled text generation, and post-editing rewrite systems that could be used to personalize NLP applications and provide users with the correct outputs based on their grammatical gender preferences. We make the Arabic Parallel Gender Corpus (APGC v2.0) publicly available

In this paper, we define the task of gender rewriting in contexts involving two users (I and/or You) -first and second grammatical persons with independent grammatical gender preferences.We focus on Arabic, a gendermarking morphologically rich language.We develop a multi-step system that combines the positive aspects of both rule-based and neural rewriting models.Our results successfully demonstrate the viability of this approach on a recently created corpus for Arabic gender rewriting, achieving 88.42 M 2 F 0.5 on a blind test set.Our proposed system improves over previous work on the first-person-only version of this task, by 3.05 absolute increase in M 2 F 0.5 .We demonstrate a use case of our gender rewriting system by using it to post-edit the output of a commercial MT system to provide personalized outputs based on the users' grammatical gender preferences.We make our code, data, and pretrained models publicly available.1

We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis. In this paper, we describe the design of CAMeL Tools and the functionalities it provides.