Abdellah El Mekki
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View article: EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs
EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs Open
Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. Despite strong performance on academic benchmarks, they often fail to tail…
View article: PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture
PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture Open
Large Language Models (LLMs) inherently reflect the vast data distributions they encounter during their pre-training phase. As this data is predominantly sourced from the web, there is a high chance it will be skewed towards high-resourced…
View article: Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset
Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset Open
Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. To address this gap, we introduce PEARL, a large-scale Arabic multimodal dataset and benchmark explic…
View article: NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities
NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities Open
Enhancing the linguistic capabilities of Large Language Models (LLMs) to include low-resource languages is a critical research area. Current research directions predominantly rely on synthetic data generated by translating English corpora,…
View article: Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs
Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs Open
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…
View article: Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs
Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs Open
View article: PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture
PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture Open
View article: Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs
Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs Open
View article: NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities
NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities Open
View article: EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs
EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs Open
View article: Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset
Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset Open
View article: Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks
Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks Open
View article: Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks
Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks Open
We introduce {\bf Swan}, a family of embedding models centred around the Arabic language, addressing both small-scale and large-scale use cases. Swan includes two variants: Swan-Small, based on ARBERTv2, and Swan-Large, built on ArMistral,…
View article: Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs
Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs Open
Large Language Models (LLMs) have demonstrated impressive performance on a wide range of natural language processing (NLP) tasks, primarily through in-context learning (ICL). In ICL, the LLM is provided with examples that represent a given…
View article: Casablanca: Data and Models for Multidialectal Arabic Speech Recognition
Casablanca: Data and Models for Multidialectal Arabic Speech Recognition Open
In spite of the recent progress in speech processing, the majority of world languages and dialects remain uncovered. This situation only furthers an already wide technological divide, thereby hindering technological and socioeconomic inclu…
View article: ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting
ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting Open
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…
View article: ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting
ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting Open
Abdellah El Mekki, Muhammad Abdul-Mageed, ElMoatez Billah Nagoudi, Ismail Berrada, Ahmed Khoumsi. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of t…
View article: CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic Open
Sarcasm is a form of figurative language where the intended meaning of a sentence differs from its literal meaning. This poses a serious challenge to several Natural Language Processing (NLP) applications such as Sentiment Analysis, Opinio…
View article: Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social Media
Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social Media Open
The prevalence of toxic content on social media platforms, such as hate speech, offensive language, and misogyny, presents serious challenges to our interconnected society. These challenging issues have attracted widespread attention in Na…
View article: UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer
UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer Open
Building real-world complex Named Entity Recognition (NER) systems is a challenging task. This is due to the complexity and ambiguity of named entities that appear in various contexts such as short input sentences, emerging entities, and c…
View article: CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic Open
Sarcasm is a form of figurative language where the intended meaning of a sentence differs from its literal meaning. This poses a serious challenge to several Natural Language Processing (NLP) applications such as Sentiment Analysis, Opinio…
View article: UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer
UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer Open
Building real-world complex Named Entity Recognition (NER) systems is a challenging task. This is due to the complexity and ambiguity of named entities that appear in various contexts such as short input sentences, emerging entities, and c…
View article: BERT-based Multi-Task Model for Country and Province Level Modern\n Standard Arabic and Dialectal Arabic Identification
BERT-based Multi-Task Model for Country and Province Level Modern\n Standard Arabic and Dialectal Arabic Identification Open
Dialect and standard language identification are crucial tasks for many\nArabic natural language processing applications. In this paper, we present our\ndeep learning-based system, submitted to the second NADI shared task for\ncountry-leve…
View article: BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification
BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification Open
Dialect and standard language identification are crucial tasks for many Arabic natural language processing applications. In this paper, we present our deep learning-based system, submitted to the second NADI shared task for country-level a…
View article: Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language Open
The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA). While previous research works tackle SA and sarcasm detection separately, this paper introduces an end-t…
View article: On the Role of Orthographic Variations in Building Multidialectal Arabic Word Embeddings
On the Role of Orthographic Variations in Building Multidialectal Arabic Word Embeddings Open
Dialectal Arabic (DA) is mostly used by over 400 million people across Arab countries as a communication channel on social media platforms, web forums, and daily life. Building Natural Language Processing systems for each DA variant is a c…
View article: An open access NLP dataset for Arabic dialects : Data collection, labeling, and model construction
An open access NLP dataset for Arabic dialects : Data collection, labeling, and model construction Open
Natural Language Processing (NLP) is today a very active field of research and innovation. Many applications need however big sets of data for supervised learning, suitably labelled for the training purpose. This includes applications for …
View article: Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding Open
Abdellah El Mekki, Abdelkader El Mahdaouy, Ismail Berrada, Ahmed Khoumsi. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.
View article: CS-UM6P at SemEval-2021 Task 1: A Deep Learning Model-based Pre-trained Transformer Encoder for Lexical Complexity
CS-UM6P at SemEval-2021 Task 1: A Deep Learning Model-based Pre-trained Transformer Encoder for Lexical Complexity Open
Lexical Complexity Prediction (LCP) involves assigning a difficulty score to a particular word or expression, in a text intended for a target audience. In this paper, we introduce a new deep learning-based system for this challenging task.…
View article: CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense
CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense Open
Humor detection has become a topic of interest for several research teams, especially those involved in socio-psychological studies, with the aim to detect the humor and the temper of a targeted population (e.g. a community, a city, a coun…