Search Results for author:
Found 32 papers, 10 papers with code
Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks
Multimodal large language models (MLLMs) have proven effective in a wide range of tasks requiring complex reasoning and linguistic comprehension.
FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models
We introduce FinTral, a suite of state-of-the-art multimodal large language models (LLMs) built upon the Mistral-7b model and tailored for financial analysis.
Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction
Our best model achieves a new SOTA on Arabic GEC, with $73. 29$ and $73. 26$ F$_{1}$ on the 2014 and 2015 QALB datasets, respectively, compared to peer-reviewed published baselines.
Violet: A Vision-Language Model for Arabic Image Captioning with Gemini Decoder
We also manually prepare a new dataset for evaluation.
NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task
We describe the findings of the fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023).
Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation
While many researchers have proposed models and solutions for individual problems, there is an acute shortage of a comprehensive Arabic natural language generation toolkit that is capable of handling a wide range of tasks.
TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties
Our evaluation covers diverse Arabic varieties such as Classical Arabic (CA), Modern Standard Arabic (MSA), and several country-level dialectal variants.
GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP
Although we further explore and confirm the utility of employing GPT-4 as a potential alternative for human evaluation, our work adds to a growing body of research underscoring the limitations of ChatGPT.
Dolphin: A Challenging and Diverse Benchmark for Arabic NLG
We present Dolphin, a novel benchmark that addresses the need for a natural language generation (NLG) evaluation framework dedicated to the wide collection of Arabic languages and varieties.
Zero-Shot Slot and Intent Detection in Low-Resource Languages
Intent detection and slot filling are critical tasks in spoken and natural language understanding for task-oriented dialog systems.
ORCA: A Challenging Benchmark for Arabic Language Understanding
Due to their crucial role in all NLP, several benchmarks have been proposed to evaluate pretrained language models.
JASMINE: Arabic GPT Models for Few-Shot Learning
Scholarship on generative pretraining (GPT) remains acutely Anglocentric, leaving serious gaps in our understanding of the whole class of autoregressive models.
A Benchmark Study of Contrastive Learning for Arabic Social Meaning
Contrastive learning (CL) brought significant progress to various NLP tasks.
TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation
We present TURJUMAN, a neural toolkit for translating from 20 languages into Modern Standard Arabic (MSA).
Decay No More: A Persistent Twitter Dataset for Learning Social Meaning
With the proliferation of social media, many studies resort to social media to construct datasets for developing social meaning understanding systems.
AraT5: Text-to-Text Transformers for Arabic Language Generation
For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks.
ARBERT \& MARBERT: Deep Bidirectional Transformers for Arabic
To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation.
Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation
Our work is in the context of the Shared Task on Machine Translation in Code-Switching.
Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing
We describe models focused at the understudied problem of translating between monolingual and code-mixed language pairs.
IndT5: A Text-to-Text Transformer for 10 Indigenous Languages
Transformer language models have become fundamental components of natural language processing based pipelines.
ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic
To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation.
DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings
We describe DiaLex, a benchmark for intrinsic evaluation of dialectal Arabic word embedding.
Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformers
We investigate different approaches to translate between similar languages under low resource conditions, as part of our contribution to the WMT 2020 Similar Languages Translation Shared Task.
Machine Generation and Detection of Arabic Manipulated and Fake News
Finally, we develop the first models for detecting manipulated Arabic news and achieve state-of-the-art results on Arabic fake news detection (macro F1=70. 06).
Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation
We describe our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE) (Mayhew et al., 2020).
Understanding and Detecting Dangerous Speech in Social Media
Social media communication has become a significant part of daily activity in modern societies.
Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19
We describe Mega-COV, a billion-scale dataset from Twitter for studying COVID-19.
AraNet: A Deep Learning Toolkit for Arabic Social Media
We describe AraNet, a collection of deep learning Arabic social media processing tools.
ArbEngVec : Arabic-English Cross-Lingual Word Embedding Model
Word Embeddings (WE) are getting increasingly popular and widely applied in many Natural Language Processing (NLP) applications due to their effectiveness in capturing semantic properties of words; Machine Translation (MT), Information Retrieval (IR) and Information Extraction (IE) are among such areas.
ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets
This article describes our proposed Arabic Sentiment Analysis system named ARB-SEN.
LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting
This article describes our proposed system named LIM-LIG.
Semantic Similarity of Arabic Sentences with Word Embeddings
Semantic textual similarity is the basis of countless applications and plays an important role in diverse areas, such as information retrieval, plagiarism detection, information extraction and machine translation.
