As large language models (LLMs) become increasingly integrated into daily life, ensuring their cultural sensitivity and inclusivity is paramount. We introduce our dataset, a year-long community-driven project covering all 22 Arab countries. The dataset includes instructions (input, response pairs) in both Modern Standard Arabic (MSA) and dialectal Arabic (DA), spanning 20 diverse topics. Built by a team of 44 researchers across the Arab world, all of whom are authors of this paper, our dataset offers a broad, inclusive perspective. We use our dataset to evaluate the cultural and dialectal capabilities of several frontier LLMs, revealing notable limitations. For instance, while closed-source LLMs generally exhibit strong performance, they are not without flaws, and smaller open-source models face greater challenges. Moreover, certain countries (e.g., Egypt, the UAE) appear better represented than others (e.g., Iraq, Mauritania, Yemen). Our annotation guidelines, code, and data for reproducibility are publicly available.

Anomaly detection in real-world time-series data is a challenging task due to the complex and nonlinear temporal dynamics involved. This paper introduces KoopAGRU, a new deep learning model designed to tackle this problem by combining Fast Fourier Transform (FFT), Deep Dynamic Mode Decomposition (DeepDMD), and Koopman theory. FFT allows KoopAGRU to decompose temporal data into time-variant and time-invariant components providing precise modeling of complex patterns. To better control these two components, KoopAGRU utilizes Gate Recurrent Unit (GRU) encoders to learn Koopman observables, enhancing the detection capability across multiple temporal scales. KoopAGRU is trained in a single process and offers fast inference times. Extensive tests on various benchmark datasets show that KoopAGRU outperforms other leading methods, achieving a new average F1-score of 90.88\% on the well-known anomalies detection task of times series datasets, and proves to be efficient and reliable in detecting anomalies in real-world scenarios.

The task of converting natural language questions (NLQs) into executable SQL queries, known as text-to-SQL, has gained significant interest in recent years, as it enables non-technical users to interact with relational databases. Many benchmarks, such as SPIDER and WikiSQL, have contributed to the development of new models and the evaluation of their performance. In addition, other datasets, like SEDE and BIRD, have introduced more challenges and complexities to better map real-world scenarios. However, these datasets primarily focus on high-resource languages such as English and Chinese. In this work, we introduce Dialect2SQL, the first large-scale, cross-domain text-to-SQL dataset in an Arabic dialect. It consists of 9,428 NLQ-SQL pairs across 69 databases in various domains. Along with SQL-related challenges such as long schemas, dirty values, and complex queries, our dataset also incorporates the complexities of the Moroccan dialect, which is known for its diverse source languages, numerous borrowed words, and unique expressions. This demonstrates that our dataset will be a valuable contribution to both the text-to-SQL community and the development of resources for low-resource languages.

Arabic encompasses a diverse array of for a select few languages. This bias towards linguistic varieties, many of which are nearly mutually resource-rich languages leaves behind the majority unintelligible (Watson, 2007; Abdul-Mageed of the world's languages (Bartelds et al., 2023; et al., 2024). This diversity includes three primary Talafha et al., 2023; Meelen et al., 2024; Tonja categories: Classical Arabic, historically used in et al., 2024). In this work, we report our efforts literature and still employed in religious contexts; to alleviate this challenge for Arabic--a collection Modern Standard Arabic (MSA), used in media, of languages and dialects spoken by more than education, and governmental settings; and numerous 450 million people. We detail a year-long community colloquial dialects, which are the main forms effort to collect and annotate a novel dataset of daily communication across the Arab world and for eight Arabic dialects spanning both Africa and often involve code-switching (Abdul-Mageed et al., Asia. This new dataset, dubbed Casablanca, is rich 2020; Mubarak et al., 2021).

The expanding financial markets of the Arab world require sophisticated Arabic NLP tools. To address this need within the banking domain, the Arabic Financial NLP (AraFinNLP) shared task proposes two subtasks: (i) Multi-dialect Intent Detection and (ii) Cross-dialect Translation and Intent Preservation. This shared task uses the updated ArBanking77 dataset, which includes about 39k parallel queries in MSA and four dialects. Each query is labeled with one or more of a common 77 intents in the banking domain. These resources aim to foster the development of robust financial Arabic NLP, particularly in the areas of machine translation and banking chat-bots. A total of 45 unique teams registered for this shared task, with 11 of them actively participated in the test phase. Specifically, 11 teams participated in Subtask 1, while only 1 team participated in Subtask 2. The winning team of Subtask 1 achieved F1 score of 0.8773, and the only team submitted in Subtask 2 achieved a 1.667 BLEU score.

Navigating the complexities of language diversity is a central challenge in developing robust natural language processing systems, especially in specialized domains like banking. The Moroccan Dialect (Darija) serves as the common language that blends cultural complexities, historical impacts, and regional differences. The complexities of Darija present a special set of challenges for language models, as it differs from Modern Standard Arabic with strong influence from French, Spanish, and Tamazight, it requires a specific approach for effective communication. To tackle these challenges, this paper introduces \textbf{DarijaBanking}, a novel Darija dataset aimed at enhancing intent classification in the banking domain, addressing the critical need for automatic banking systems (e.g., chatbots) that communicate in the native language of Moroccan clients. DarijaBanking comprises over 1,800 parallel high-quality queries in Darija, Modern Standard Arabic (MSA), English, and French, organized into 24 intent classes. We experimented with various intent classification methods, including full fine-tuning of monolingual and multilingual models, zero-shot learning, retrieval-based approaches, and Large Language Model prompting. One of the main contributions of this work is BERTouch, our BERT-based language model for intent classification in Darija. BERTouch achieved F1-scores of 0.98 for Darija and 0.96 for MSA on DarijaBanking, outperforming the state-of-the-art alternatives including GPT-4 showcasing its effectiveness in the targeted application.

Automatic diacritization of Arabic text involves adding diacritical marks (diacritics) to the text. This task poses a significant challenge with noteworthy implications for computational processing and comprehension. In this paper, we introduce PTCAD (Pre-FineTuned Token Classification for Arabic Diacritization, a novel two-phase approach for the Arabic Text Diacritization task. PTCAD comprises a pre-finetuning phase and a finetuning phase, treating Arabic Text Diacritization as a token classification task for pre-trained models. The effectiveness of PTCAD is demonstrated through evaluations on two benchmark datasets derived from the Tashkeela dataset, where it achieves state-of-the-art results, including a 20\% reduction in Word Error Rate (WER) compared to existing benchmarks and superior performance over GPT-4 in ATD tasks.

Bilingual Lexicon Induction (BLI), where words are translated between two languages, is an important NLP task. While noticeable progress on BLI in rich resource languages using static word embeddings has been achieved. The word translation performance can be further improved by incorporating information from contextualized word embeddings. In this paper, we introduce ProMap, a novel approach for BLI that leverages the power of prompting pretrained multilingual and multidialectal language models to address these challenges. To overcome the employment of subword tokens in these models, ProMap relies on an effective padded prompting of language models with a seed dictionary that achieves good performance when used independently. We also demonstrate the effectiveness of ProMap in re-ranking results from other BLI methods such as with aligned static word embeddings. When evaluated on both rich-resource and low-resource languages, ProMap consistently achieves state-of-the-art results. Furthermore, ProMap enables strong performance in few-shot scenarios (even with less than 10 training examples), making it a valuable tool for low-resource language translation. Overall, we believe our method offers both exciting and promising direction for BLI in general and low-resource languages in particular. ProMap code and data are available at \url{https://github.com/4mekki4/promap}.