El Moatez Billah Nagoudi

Also published as: El-Moatez-Billah Nagoudi


2021

pdf bib
Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing
Ganesh Jawahar | El Moatez Billah Nagoudi | Muhammad Abdul-Mageed | Laks Lakshmanan, V.S.
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching

We describe models focused at the understudied problem of translating between monolingual and code-mixed language pairs. More specifically, we offer a wide range of models that convert monolingual English text into Hinglish (code-mixed Hindi and English). Given the recent success of pretrained language models, we also test the utility of two recent Transformer-based encoder-decoder models (i.e., mT5 and mBART) on the task finding both to work well. Given the paucity of training data for code-mixing, we also propose a dependency-free method for generating code-mixed texts from bilingual distributed representations that we exploit for improving language model performance. In particular, armed with this additional data, we adopt a curriculum learning approach where we first finetune the language models on synthetic data then on gold code-mixed data. We find that, although simple, our synthetic code-mixing method is competitive with (and in some cases is even superior to) several standard methods (backtranslation, method based on equivalence constraint theory) under a diverse set of conditions. Our work shows that the mT5 model, finetuned following the curriculum learning procedure, achieves best translation performance (12.67 BLEU). Our models place first in the overall ranking of the English-Hinglish official shared task.

pdf bib
Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation
El Moatez Billah Nagoudi | AbdelRahim Elmadany | Muhammad Abdul-Mageed
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching

Recent progress in neural machine translation (NMT) has made it possible to translate successfully between monolingual language pairs where large parallel data exist, with pre-trained models improving performance even further. Although there exists work on translating in code-mixed settings (where one of the pairs includes text from two or more languages), it is still unclear what recent success in NMT and language modeling exactly means for translating code-mixed text. We investigate one such context, namely MT from code-mixed Modern Standard Arabic and Egyptian Arabic (MSAEA) into English. We develop models under different conditions, employing both (i) standard end-to-end sequence-to-sequence (S2S) Transformers trained from scratch and (ii) pre-trained S2S language models (LMs). We are able to acquire reasonable performance using only MSA-EN parallel data with S2S models trained from scratch. We also find LMs fine-tuned on data from various Arabic dialects to help the MSAEA-EN task. Our work is in the context of the Shared Task on Machine Translation in Code-Switching. Our best model achieves 25.72 BLEU, placing us first on the official shared task evaluation for MSAEA-EN.

pdf bib
Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19
Muhammad Abdul-Mageed | AbdelRahim Elmadany | El Moatez Billah Nagoudi | Dinesh Pabbi | Kunal Verma | Rannie Lin
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We describe Mega-COV, a billion-scale dataset from Twitter for studying COVID-19. The dataset is diverse (covers 268 countries), longitudinal (goes as back as 2007), multilingual (comes in 100+ languages), and has a significant number of location-tagged tweets (~169M tweets). We release tweet IDs from the dataset. We also develop two powerful models, one for identifying whether or not a tweet is related to the pandemic (best F1=97%) and another for detecting misinformation about COVID-19 (best F1=92%). A human annotation study reveals the utility of our models on a subset of Mega-COV. Our data and models can be useful for studying a wide host of phenomena related to the pandemic. Mega-COV and our models are publicly available.

pdf bib
IndT5: A Text-to-Text Transformer for 10 Indigenous Languages
El Moatez Billah Nagoudi | Wei-Rui Chen | Muhammad Abdul-Mageed | Hasan Cavusoglu
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

Transformer language models have become fundamental components of NLP based pipelines. Although several Transformer have been introduced to serve many languages, there is a shortage of models pre-trained for low-resource and Indigenous languages in particular. In this work, we introduce IndT5, the first Transformer language model for Indigenous languages. To train IndT5, we build IndCorpus, a new corpus for 10 Indigenous languages and Spanish. We also present the application of IndT5 to machine translation by investigating different approaches to translate between Spanish and the Indigenous languages as part of our contribution to the AmericasNLP 2021 Shared Task on Open Machine Translation. IndT5 and IndCorpus are publicly available for research.

pdf bib
DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings
Muhammad Abdul-Mageed | Shady Elbassuoni | Jad Doughman | AbdelRahim Elmadany | El Moatez Billah Nagoudi | Yorgo Zoughby | Ahmad Shaher | Iskander Gaba | Ahmed Helal | Mohammed El-Razzaz
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Word embeddings are a core component of modern natural language processing systems, making the ability to thoroughly evaluate them a vital task. We describe DiaLex, a benchmark for intrinsic evaluation of dialectal Arabic word embeddings. DiaLex covers five important Arabic dialects: Algerian, Egyptian, Lebanese, Syrian, and Tunisian. Across these dialects, DiaLex provides a testbank for six syntactic and semantic relations, namely male to female, singular to dual, singular to plural, antonym, comparative, and genitive to past tense. DiaLex thus consists of a collection of word pairs representing each of the six relations in each of the five dialects. To demonstrate the utility of DiaLex, we use it to evaluate a set of existing and new Arabic word embeddings that we developed. Beyond evaluation of word embeddings, DiaLex supports efforts to integrate dialects into the Arabic language curriculum. It can be easily translated into Modern Standard Arabic and English, which can be useful for evaluating word translation. Our benchmark, evaluation code, and new word embedding models will be publicly available.

pdf bib
ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic
Muhammad Abdul-Mageed | AbdelRahim Elmadany | El Moatez Billah Nagoudi
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large ( 3.4x larger size). Our models are publicly available at https://github.com/UBC-NLP/marbert and ARLUE will be released through the same repository.

2020

pdf bib
Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformers
Ife Adebara | El Moatez Billah Nagoudi | Muhammad Abdul Mageed
Proceedings of the Fifth Conference on Machine Translation

In this work we investigate different approaches to translate between similar languages despite low resource limitations. This work is done as the participation of the UBC NLP research group in the WMT 2019 Similar Languages Translation Shared Task. We participated in all language pairs and performed various experiments. We used a transformer architecture for all the models and used back-translation for one of the language pairs. We explore both bilingual and multi-lingual approaches. We describe the pre-processing, training, translation and results for each model. We also investigate the role of mutual intelligibility in model performance.

pdf bib
Machine Generation and Detection of Arabic Manipulated and Fake News
El Moatez Billah Nagoudi | AbdelRahim Elmadany | Muhammad Abdul-Mageed | Tariq Alhindi
Proceedings of the Fifth Arabic Natural Language Processing Workshop

Fake news and deceptive machine-generated text are serious problems threatening modern societies, including in the Arab world. This motivates work on detecting false and manipulated stories online. However, a bottleneck for this research is lack of sufficient data to train detection models. We present a novel method for automatically generating Arabic manipulated (and potentially fake) news stories. Our method is simple and only depends on availability of true stories, which are abundant online, and a part of speech tagger (POS). To facilitate future work, we dispense with both of these requirements altogether by providing AraNews, a novel and large POS-tagged news dataset that can be used off-the-shelf. Using stories generated based on AraNews, we carry out a human annotation study that casts light on the effects of machine manipulation on text veracity. The study also measures human ability to detect Arabic machine manipulated text generated by our method. 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). Our models and data are publicly available.

pdf bib
Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation
El Moatez Billah Nagoudi | Muhammad Abdul-Mageed | Hasan Cavusoglu
Proceedings of the Fourth Workshop on Neural Generation and Translation

We describe our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE). We view MT models at various training stages (i.e., checkpoints) as human learners at different levels. Hence, we employ an ensemble of multi-checkpoints from the same model to generate translation sequences with various levels of fluency. From each checkpoint, for our best model, we sample n-Best sequences (n=10) with a beam width =100. We achieve an 37.57 macro F1 with a 6 checkpoint model ensemble on the official shared task test data, outperforming a baseline Amazon translation system of 21.30 macro F1 and ultimately demonstrating the utility of our intuitive method.

pdf bib
AraNet: A Deep Learning Toolkit for Arabic Social Media
Muhammad Abdul-Mageed | Chiyu Zhang | Azadeh Hashemi | El Moatez Billah Nagoudi
Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection

We describe AraNet, a collection of deep learning Arabic social media processing tools. Namely, we exploit an extensive host of both publicly available and novel social media datasets to train bidirectional encoders from transformers (BERT) focused at social meaning extraction. AraNet models predict age, dialect, gender, emotion, irony, and sentiment. AraNet either delivers state-of-the-art performance on a number of these tasks and performs competitively on others. AraNet is exclusively based on a deep learning framework, giving it the advantage of being feature-engineering free. To the best of our knowledge, AraNet is the first to performs predictions across such a wide range of tasks for Arabic NLP. As such, AraNet has the potential to meet critical needs. We publicly release AraNet to accelerate research, and to facilitate model-based comparisons across the different tasks

pdf bib
Understanding and Detecting Dangerous Speech in Social Media
Ali Alshehri | El Moatez Billah Nagoudi | Muhammad Abdul-Mageed
Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection

Social media communication has become a significant part of daily activity in modern societies. For this reason, ensuring safety in social media platforms is a necessity. Use of dangerous language such as physical threats in online environments is a somewhat rare, yet remains highly important. Although several works have been performed on the related issue of detecting offensive and hateful language, dangerous speech has not previously been treated in any significant way. Motivated by these observations, we report our efforts to build a labeled dataset for dangerous speech. We also exploit our dataset to develop highly effective models to detect dangerous content. Our best model performs at 59.60% macro F1, significantly outperforming a competitive baseline.

2019

pdf bib
ArbEngVec : Arabic-English Cross-Lingual Word Embedding Model
Raki Lachraf | El Moatez Billah Nagoudi | Youcef Ayachi | Ahmed Abdelali | Didier Schwab
Proceedings of the Fourth Arabic Natural Language Processing Workshop

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. In this paper, we propose an open source ArbEngVec which provides several Arabic-English cross-lingual word embedding models. To train our bilingual models, we use a large dataset with more than 93 million pairs of Arabic-English parallel sentences. In addition, we perform both extrinsic and intrinsic evaluations for the different word embedding model variants. The extrinsic evaluation assesses the performance of models on the cross-language Semantic Textual Similarity (STS), while the intrinsic evaluation is based on the Word Translation (WT) task.

2018

pdf bib
ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets
El Moatez Billah Nagoudi
Proceedings of The 12th International Workshop on Semantic Evaluation

This article describes our proposed Arabic Sentiment Analysis system named ARB-SEN. This system is designed for the International Workshop on Semantic Evaluation 2018 (SemEval-2018), Task1: Affect in Tweets. ARB-SEN proposes two supervised models to estimate the sentiment intensity in Arabic tweets. Both models use a set of features including sentiment lexicon, negation, word embedding and emotion symbols features. Our system combines these features to assist the sentiment analysis task. ARB-SEN system achieves a correlation score of 0.720, ranking 6th among all participants in the valence intensity regression (V-reg) for the Arabic sub-task organized within the SemEval 2018 evaluation campaign.

2017

pdf bib
Amélioration de la similarité sémantique vectorielle par méthodes non-supervisées (Improved the Semantic Similarity with Weighting Vectors)
El-Moatez-Billah Nagoudi | Jérémy Ferrero | Didier Schwab
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts

pdf bib
Semantic Similarity of Arabic Sentences with Word Embeddings
El Moatez Billah Nagoudi | Didier Schwab
Proceedings of the Third Arabic Natural Language Processing Workshop

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. This article proposes an innovative word embedding-based system devoted to calculate the semantic similarity in Arabic sentences. The main idea is to exploit vectors as word representations in a multidimensional space in order to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. The performance of our proposed system is confirmed through the Pearson correlation between our assigned semantic similarity scores and human judgments.

pdf bib
LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting
El Moatez Billah Nagoudi | Jérémy Ferrero | Didier Schwab
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This article describes our proposed system named LIM-LIG. This system is designed for SemEval 2017 Task1: Semantic Textual Similarity (Track1). LIM-LIG proposes an innovative enhancement to word embedding-based model devoted to measure the semantic similarity in Arabic sentences. The main idea is to exploit the word representations as vectors in a multidimensional space to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. LIM-LIG system achieves a Pearson’s correlation of 0.74633, ranking 2nd among all participants in the Arabic monolingual pairs STS task organized within the SemEval 2017 evaluation campaign