Ahmed Abdelali

Also published as: Ahmed AbdelAli


2021

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Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
Firoj Alam | Shaden Shaar | Fahim Dalvi | Hassan Sajjad | Alex Nikolov | Hamdy Mubarak | Giovanni Da San Martino | Ahmed Abdelali | Nadir Durrani | Kareem Darwish | Abdulaziz Al-Homaid | Wajdi Zaghouani | Tommaso Caselli | Gijs Danoe | Friso Stolk | Britt Bruntink | Preslav Nakov
Findings of the Association for Computational Linguistics: EMNLP 2021

With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.

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ASAD: Arabic Social media Analytics and unDerstanding
Sabit Hassan | Hamdy Mubarak | Ahmed Abdelali | Kareem Darwish
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

This system demonstration paper describes ASAD: Arabic Social media Analysis and unDerstanding, a suite of seven individual modules that allows users to determine dialects, sentiment, news category, offensiveness, hate speech, adult content, and spam in Arabic tweets. The suite is made available through a web API and a web interface where users can enter text or upload files.

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QADI: Arabic Dialect Identification in the Wild
Ahmed Abdelali | Hamdy Mubarak | Younes Samih | Sabit Hassan | Kareem Darwish
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Proper dialect identification is important for a variety of Arabic NLP applications. In this paper, we present a method for rapidly constructing a tweet dataset containing a wide range of country-level Arabic dialects —covering 18 different countries in the Middle East and North Africa region. Our method relies on applying multiple filters to identify users who belong to different countries based on their account descriptions and to eliminate tweets that either write mainly in Modern Standard Arabic or mostly use vulgar language. The resultant dataset contains 540k tweets from 2,525 users who are evenly distributed across 18 Arab countries. Using intrinsic evaluation, we show that the labels of a set of randomly selected tweets are 91.5% accurate. For extrinsic evaluation, we are able to build effective country level dialect identification on tweets with a macro-averaged F1-score of 60.6% across 18 classes.

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Arabic Offensive Language on Twitter: Analysis and Experiments
Hamdy Mubarak | Ammar Rashed | Kareem Darwish | Younes Samih | Ahmed Abdelali
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arabic offensive tweet dataset. We introduce a method for building a dataset that is not biased by topic, dialect, or target. We produce the largest Arabic dataset to date with special tags for vulgarity and hate speech. We thoroughly analyze the dataset to determine which topics, dialects, and gender are most associated with offensive tweets and how Arabic speakers useoffensive language. Lastly, we conduct many experiments to produce strong results (F1 =83.2) on the dataset using SOTA techniques.

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Adult Content Detection on Arabic Twitter: Analysis and Experiments
Hamdy Mubarak | Sabit Hassan | Ahmed Abdelali
Proceedings of the Sixth Arabic Natural Language Processing Workshop

With Twitter being one of the most popular social media platforms in the Arab region, it is not surprising to find accounts that post adult content in Arabic tweets; despite the fact that these platforms dissuade users from such content. In this paper, we present a dataset of Twitter accounts that post adult content. We perform an in-depth analysis of the nature of this data and contrast it with normal tweet content. Additionally, we present extensive experiments with traditional machine learning models, deep neural networks and contextual embeddings to identify such accounts. We show that from user information alone, we can identify such accounts with F1 score of 94.7% (macro average). With the addition of only one tweet as input, the F1 score rises to 96.8%.

2020

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ALT at SemEval-2020 Task 12: Arabic and English Offensive Language Identification in Social Media
Sabit Hassan | Younes Samih | Hamdy Mubarak | Ahmed Abdelali
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. We focus on sub-task A (Offensive Language Identification) for two languages: Arabic and English. Our efforts for both languages achieved more than 90% macro-averaged F1-score on the official test set. For Arabic, the best results were obtained by a system combination of Support Vector Machine, Deep Neural Network, and fine-tuned Bidirectional Encoder Representations from Transformers (BERT). For English, the best results were obtained by fine-tuning BERT.

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Constructing a Bilingual Corpus of Parallel Tweets
Hamdy Mubarak | Sabit Hassan | Ahmed Abdelali
Proceedings of the 13th Workshop on Building and Using Comparable Corpora

In a bid to reach a larger and more diverse audience, Twitter users often post parallel tweets—tweets that contain the same content but are written in different languages. Parallel tweets can be an important resource for developing machine translation (MT) systems among other natural language processing (NLP) tasks. In this paper, we introduce a generic method for collecting parallel tweets. Using this method, we collect a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts who post English-Arabictweets regularly. Since our method is generic, it can also be used for collecting parallel tweets that cover less-resourced languages such as Serbian and Urdu. Additionally, we annotate a subset of Twitter accounts with their countries of origin and topic of interest, which provides insights about the population who post parallel tweets. This latter information can also be useful for author profiling tasks.

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Bert Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets
Fouzi Harrag | Maria Dabbah | Kareem Darwish | Ahmed Abdelali
Proceedings of the Fifth Arabic Natural Language Processing Workshop

During the last two decades, we have progressively turned to the Internet and social media to find news, entertain conversations and share opinion. Recently, OpenAI has developed a machine learning system called GPT-2 for Generative Pre-trained Transformer-2, which can produce deepfake texts. It can generate blocks of text based on brief writing prompts that look like they were written by humans, facilitating the spread false or auto-generated text. In line with this progress, and in order to counteract potential dangers, several methods have been proposed for detecting text written by these language models. In this paper, we propose a transfer learning based model that will be able to detect if an Arabic sentence is written by humans or automatically generated by bots. Our dataset is based on tweets from a previous work, which we have crawled and extended using the Twitter API. We used GPT2-Small-Arabic to generate fake Arabic Sentences. For evaluation, we compared different recurrent neural network (RNN) word embeddings based baseline models, namely: LSTM, BI-LSTM, GRU and BI-GRU, with a transformer-based model. Our new transfer-learning model has obtained an accuracy up to 98%. To the best of our knowledge, this work is the first study where ARABERT and GPT2 were combined to detect and classify the Arabic auto-generated texts.

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Improving Arabic Text Categorization Using Transformer Training Diversification
Shammur Absar Chowdhury | Ahmed Abdelali | Kareem Darwish | Jung Soon-Gyo | Joni Salminen | Bernard J. Jansen
Proceedings of the Fifth Arabic Natural Language Processing Workshop

Automatic categorization of short texts, such as news headlines and social media posts, has many applications ranging from content analysis to recommendation systems. In this paper, we use such text categorization i.e., labeling the social media posts to categories like ‘sports’, ‘politics’, ‘human-rights’ among others, to showcase the efficacy of models across different sources and varieties of Arabic. In doing so, we show that diversifying the training data, whether by using diverse training data for the specific task (an increase of 21% macro F1) or using diverse data to pre-train a BERT model (26% macro F1), leads to overall improvements in classification effectiveness. In our work, we also introduce two new Arabic text categorization datasets, where the first is composed of social media posts from a popular Arabic news channel that cover Twitter, Facebook, and YouTube, and the second is composed of tweets from popular Arabic accounts. The posts in the former are nearly exclusively authored in modern standard Arabic (MSA), while the tweets in the latter contain both MSA and dialectal Arabic.

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AraBench: Benchmarking Dialectal Arabic-English Machine Translation
Hassan Sajjad | Ahmed Abdelali | Nadir Durrani | Fahim Dalvi
Proceedings of the 28th International Conference on Computational Linguistics

Low-resource machine translation suffers from the scarcity of training data and the unavailability of standard evaluation sets. While a number of research efforts target the former, the unavailability of evaluation benchmarks remain a major hindrance in tracking the progress in low-resource machine translation. In this paper, we introduce AraBench, an evaluation suite for dialectal Arabic to English machine translation. Compared to Modern Standard Arabic, Arabic dialects are challenging due to their spoken nature, non-standard orthography, and a large variation in dialectness. To this end, we pool together already available Dialectal Arabic-English resources and additionally build novel test sets. AraBench offers 4 coarse, 15 fine-grained and 25 city-level dialect categories, belonging to diverse genres, such as media, chat, religion and travel with varying level of dialectness. We report strong baselines using several training settings: fine-tuning, back-translation and data augmentation. The evaluation suite opens a wide range of research frontiers to push efforts in low-resource machine translation, particularly Arabic dialect translation. The evaluation suite and the dialectal system are publicly available for research purposes.

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Arabic Curriculum Analysis
Hamdy Mubarak | Shimaa Amer | Ahmed Abdelali | Kareem Darwish
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

Developing a platform that analyzes the content of curricula can help identify their shortcomings and whether they are tailored to specific desired outcomes. In this paper, we present a system to analyze Arabic curricula and provide insights into their content. It allows users to explore word presence, surface-forms used, as well as contrasting statistics between different countries from which the curricula were selected. Also, it provides a facility to grade text in reference to given grade-level and gives users feedback about the complexity or difficulty of words used in a text.

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ALT Submission for OSACT Shared Task on Offensive Language Detection
Sabit Hassan | Younes Samih | Hamdy Mubarak | Ahmed Abdelali | Ammar Rashed | Shammur Absar Chowdhury
Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection

In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51% on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63% on the test set.

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A Multi-Platform Arabic News Comment Dataset for Offensive Language Detection
Shammur Absar Chowdhury | Hamdy Mubarak | Ahmed Abdelali | Soon-gyo Jung | Bernard J. Jansen | Joni Salminen
Proceedings of the 12th Language Resources and Evaluation Conference

Access to social media often enables users to engage in conversation with limited accountability. This allows a user to share their opinions and ideology, especially regarding public content, occasionally adopting offensive language. This may encourage hate crimes or cause mental harm to targeted individuals or groups. Hence, it is important to detect offensive comments in social media platforms. Typically, most studies focus on offensive commenting in one platform only, even though the problem of offensive language is observed across multiple platforms. Therefore, in this paper, we introduce and make publicly available a new dialectal Arabic news comment dataset, collected from multiple social media platforms, including Twitter, Facebook, and YouTube. We follow two-step crowd-annotator selection criteria for low-representative language annotation task in a crowdsourcing platform. Furthermore, we analyze the distinctive lexical content along with the use of emojis in offensive comments. We train and evaluate the classifiers using the annotated multi-platform dataset along with other publicly available data. Our results highlight the importance of multiple platform dataset for (a) cross-platform, (b) cross-domain, and (c) cross-dialect generalization of classifier performance.

2019

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A System for Diacritizing Four Varieties of Arabic
Hamdy Mubarak | Ahmed Abdelali | Kareem Darwish | Mohamed Eldesouki | Younes Samih | Hassan Sajjad
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Short vowels, aka diacritics, are more often omitted when writing different varieties of Arabic including Modern Standard Arabic (MSA), Classical Arabic (CA), and Dialectal Arabic (DA). However, diacritics are required to properly pronounce words, which makes diacritic restoration (a.k.a. diacritization) essential for language learning and text-to-speech applications. In this paper, we present a system for diacritizing MSA, CA, and two varieties of DA, namely Moroccan and Tunisian. The system uses a character level sequence-to-sequence deep learning model that requires no feature engineering and beats all previous SOTA systems for all the Arabic varieties that we test on.

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POS Tagging for Improving Code-Switching Identification in Arabic
Mohammed Attia | Younes Samih | Ali Elkahky | Hamdy Mubarak | Ahmed Abdelali | Kareem Darwish
Proceedings of the Fourth Arabic Natural Language Processing Workshop

When speakers code-switch between their native language and a second language or language variant, they follow a syntactic pattern where words and phrases from the embedded language are inserted into the matrix language. This paper explores the possibility of utilizing this pattern in improving code-switching identification between Modern Standard Arabic (MSA) and Egyptian Arabic (EA). We try to answer the question of how strong is the POS signal in word-level code-switching identification. We build a deep learning model enriched with linguistic features (including POS tags) that outperforms the state-of-the-art results by 1.9% on the development set and 1.0% on the test set. We also show that in intra-sentential code-switching, the selection of lexical items is constrained by POS categories, where function words tend to come more often from the dialectal language while the majority of content words come from the standard language.

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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.

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QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification
Younes Samih | Hamdy Mubarak | Ahmed Abdelali | Mohammed Attia | Mohamed Eldesouki | Kareem Darwish
Proceedings of the Fourth Arabic Natural Language Processing Workshop

This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification). In our participation in both subtasks, we explored a number of approaches and system combinations to obtain the best performance for both tasks. These include deep neural nets and heuristics. Since individual approaches suffer from various shortcomings, the combination of different approaches was able to fill some of these gaps. Our system achieves F1-Scores of 66.1% and 67.0% on the development sets for Subtasks 1 and 2 respectively.

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Human-Informed Speakers and Interpreters Analysis in the WAW Corpus and an Automatic Method for Calculating Interpreters’ Décalage
Irina Temnikova | Ahmed Abdelali | Souhila Djabri | Samy Hedaya
Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)

This article presents a multi-faceted analysis of a subset of interpreted conference speeches from the WAW corpus for the English-Arabic language pair. We analyze several speakers and interpreters variables via manual annotation and automatic methods. We propose a new automatic method for calculating interpreters’ décalage based on Automatic Speech Recognition (ASR) and automatic alignment of named entities and content words between speaker and interpreter. The method is evaluated by two human annotators who have expertise in interpreting and Interpreting Studies and shows highly satisfactory results, accompanied with a high inter-annotator agreement. We provide insights about the relations of speakers’ variables, interpreters’ variables and décalage and discuss them from Interpreting Studies and interpreting practice point of view. We had interesting findings about interpreters behavior which need to be extended to a large number of conference sessions in our future research.

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Highly Effective Arabic Diacritization using Sequence to Sequence Modeling
Hamdy Mubarak | Ahmed Abdelali | Hassan Sajjad | Younes Samih | Kareem Darwish
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Arabic text is typically written without short vowels (or diacritics). However, their presence is required for properly verbalizing Arabic and is hence essential for applications such as text to speech. There are two types of diacritics, namely core-word diacritics and case-endings. Most previous works on automatic Arabic diacritic recovery rely on a large number of manually engineered features, particularly for case-endings. In this work, we present a unified character level sequence-to-sequence deep learning model that recovers both types of diacritics without the use of explicit feature engineering. Specifically, we employ a standard neural machine translation setup on overlapping windows of words (broken down into characters), and then we use voting to select the most likely diacritized form of a word. The proposed model outperforms all previous state-of-the-art systems. Our best settings achieve a word error rate (WER) of 4.49% compared to the state-of-the-art of 12.25% on a standard dataset.

2018

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Multi-Dialect Arabic POS Tagging: A CRF Approach
Kareem Darwish | Hamdy Mubarak | Ahmed Abdelali | Mohamed Eldesouki | Younes Samih | Randah Alharbi | Mohammed Attia | Walid Magdy | Laura Kallmeyer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic
Ahmed Abdelali | Irina Temnikova | Samy Hedaya | Stephan Vogel
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Part-of-Speech Tagging for Arabic Gulf Dialect Using Bi-LSTM
Randah Alharbi | Walid Magdy | Kareem Darwish | Ahmed AbdelAli | Hamdy Mubarak
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging
Hassan Sajjad | Fahim Dalvi | Nadir Durrani | Ahmed Abdelali | Yonatan Belinkov | Stephan Vogel
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Word segmentation plays a pivotal role in improving any Arabic NLP application. Therefore, a lot of research has been spent in improving its accuracy. Off-the-shelf tools, however, are: i) complicated to use and ii) domain/dialect dependent. We explore three language-independent alternatives to morphological segmentation using: i) data-driven sub-word units, ii) characters as a unit of learning, and iii) word embeddings learned using a character CNN (Convolution Neural Network). On the tasks of Machine Translation and POS tagging, we found these methods to achieve close to, and occasionally surpass state-of-the-art performance. In our analysis, we show that a neural machine translation system is sensitive to the ratio of source and target tokens, and a ratio close to 1 or greater, gives optimal performance.

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Learning from Relatives: Unified Dialectal Arabic Segmentation
Younes Samih | Mohamed Eldesouki | Mohammed Attia | Kareem Darwish | Ahmed Abdelali | Hamdy Mubarak | Laura Kallmeyer
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Arabic dialects do not just share a common koiné, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other. In this paper we build a unified segmentation model where the training data for different dialects are combined and a single model is trained. The model yields higher accuracies than dialect-specific models, eliminating the need for dialect identification before segmentation. We also measure the degree of relatedness between four major Arabic dialects by testing how a segmentation model trained on one dialect performs on the other dialects. We found that linguistic relatedness is contingent with geographical proximity. In our experiments we use SVM-based ranking and bi-LSTM-CRF sequence labeling.

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Arabic Diacritization: Stats, Rules, and Hacks
Kareem Darwish | Hamdy Mubarak | Ahmed Abdelali
Proceedings of the Third Arabic Natural Language Processing Workshop

In this paper, we present a new and fast state-of-the-art Arabic diacritizer that guesses the diacritics of words and then their case endings. We employ a Viterbi decoder at word-level with back-off to stem, morphological patterns, and transliteration and sequence labeling based diacritization of named entities. For case endings, we use Support Vector Machine (SVM) based ranking coupled with morphological patterns and linguistic rules to properly guess case endings. We achieve a low word level diacritization error of 3.29% and 12.77% without and with case endings respectively on a new multi-genre free of copyright test set. We are making the diacritizer available for free for research purposes.

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A Neural Architecture for Dialectal Arabic Segmentation
Younes Samih | Mohammed Attia | Mohamed Eldesouki | Ahmed Abdelali | Hamdy Mubarak | Laura Kallmeyer | Kareem Darwish
Proceedings of the Third Arabic Natural Language Processing Workshop

The automated processing of Arabic Dialects is challenging due to the lack of spelling standards and to the scarcity of annotated data and resources in general. Segmentation of words into its constituent parts is an important processing building block. In this paper, we show how a segmenter can be trained using only 350 annotated tweets using neural networks without any normalization or use of lexical features or lexical resources. We deal with segmentation as a sequence labeling problem at the character level. We show experimentally that our model can rival state-of-the-art methods that rely on additional resources.

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Arabic POS Tagging: Don’t Abandon Feature Engineering Just Yet
Kareem Darwish | Hamdy Mubarak | Ahmed Abdelali | Mohamed Eldesouki
Proceedings of the Third Arabic Natural Language Processing Workshop

This paper focuses on comparing between using Support Vector Machine based ranking (SVM-Rank) and Bidirectional Long-Short-Term-Memory (bi-LSTM) neural-network based sequence labeling in building a state-of-the-art Arabic part-of-speech tagging system. Using SVM-Rank leads to state-of-the-art results, but with a fair amount of feature engineering. Using bi-LSTM, particularly when combined with word embeddings, may lead to competitive POS-tagging results by automatically deducing latent linguistic features. However, we show that augmenting bi-LSTM sequence labeling with some of the features that we used for the SVM-Rank based tagger yields to further improvements. We also show that gains that realized by using embeddings may not be additive with the gains achieved by the features. We are open-sourcing both the SVM-Rank and the bi-LSTM based systems for free.

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Interpreting Strategies Annotation in the WAW Corpus
Irina Temnikova | Ahmed Abdelali | Samy Hedaya | Stephan Vogel | Aishah Al Daher
Proceedings of the Workshop Human-Informed Translation and Interpreting Technology

With the aim to teach our automatic speech-to-text translation system human interpreting strategies, our first step is to identify which interpreting strategies are most often used in the language pair of our interest (English-Arabic). In this article we run an automatic analysis of a corpus of parallel speeches and their human interpretations, and provide the results of manually annotating the human interpreting strategies in a sample of the corpus. We give a glimpse of the corpus, whose value surpasses the fact that it contains a high number of scientific speeches with their interpretations from English into Arabic, as it also provides rich information about the interpreters. We also discuss the difficulties, which we encountered on our way, as well as our solutions to them: our methodology for manual re-segmentation and alignment of parallel segments, the choice of annotation tool, and the annotation procedure. Our annotation findings explain the previously extracted specific statistical features of the interpreted corpus (compared with a translation one) as well as the quality of interpretation provided by different interpreters.

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QCRI Live Speech Translation System
Fahim Dalvi | Yifan Zhang | Sameer Khurana | Nadir Durrani | Hassan Sajjad | Ahmed Abdelali | Hamdy Mubarak | Ahmed Ali | Stephan Vogel
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

This paper presents QCRI’s Arabic-to-English live speech translation system. It features modern web technologies to capture live audio, and broadcasts Arabic transcriptions and English translations simultaneously. Our Kaldi-based ASR system uses the Time Delay Neural Network (TDNN) architecture, while our Machine Translation (MT) system uses both phrase-based and neural frameworks. Although our neural MT system is slower than the phrase-based system, it produces significantly better translations and is memory efficient. The demo is available at https://st.qcri.org/demos/livetranslation.

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The SUMMA Platform Prototype
Renars Liepins | Ulrich Germann | Guntis Barzdins | Alexandra Birch | Steve Renals | Susanne Weber | Peggy van der Kreeft | Hervé Bourlard | João Prieto | Ondřej Klejch | Peter Bell | Alexandros Lazaridis | Alfonso Mendes | Sebastian Riedel | Mariana S. C. Almeida | Pedro Balage | Shay B. Cohen | Tomasz Dwojak | Philip N. Garner | Andreas Giefer | Marcin Junczys-Dowmunt | Hina Imran | David Nogueira | Ahmed Ali | Sebastião Miranda | Andrei Popescu-Belis | Lesly Miculicich Werlen | Nikos Papasarantopoulos | Abiola Obamuyide | Clive Jones | Fahim Dalvi | Andreas Vlachos | Yang Wang | Sibo Tong | Rico Sennrich | Nikolaos Pappas | Shashi Narayan | Marco Damonte | Nadir Durrani | Sameer Khurana | Ahmed Abdelali | Hassan Sajjad | Stephan Vogel | David Sheppey | Chris Hernon | Jeff Mitchell
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.

2016

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Eyes Don’t Lie: Predicting Machine Translation Quality Using Eye Movement
Hassan Sajjad | Francisco Guzmán | Nadir Durrani | Ahmed Abdelali | Houda Bouamor | Irina Temnikova | Stephan Vogel
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Farasa: A Fast and Furious Segmenter for Arabic
Ahmed Abdelali | Kareem Darwish | Nadir Durrani | Hamdy Mubarak
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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iAppraise: A Manual Machine Translation Evaluation Environment Supporting Eye-tracking
Ahmed Abdelali | Nadir Durrani | Francisco Guzmán
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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A Deep Fusion Model for Domain Adaptation in Phrase-based MT
Nadir Durrani | Hassan Sajjad | Shafiq Joty | Ahmed Abdelali
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We present a novel fusion model for domain adaptation in Statistical Machine Translation. Our model is based on the joint source-target neural network Devlin et al., 2014, and is learned by fusing in- and out-domain models. The adaptation is performed by backpropagating errors from the output layer to the word embedding layer of each model, subsequently adjusting parameters of the composite model towards the in-domain data. On the standard tasks of translating English-to-German and Arabic-to-English TED talks, we observed average improvements of +0.9 and +0.7 BLEU points, respectively over a competition grade phrase-based system. We also demonstrate improvements over existing adaptation methods.

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Arabic to English Person Name Transliteration using Twitter
Hamdy Mubarak | Ahmed Abdelali
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Social media outlets are providing new opportunities for harvesting valuable resources. We present a novel approach for mining data from Twitter for the purpose of building transliteration resources and systems. Such resources are crucial in translation and retrieval tasks. We demonstrate the benefits of the approach on Arabic to English transliteration. The contribution of this approach includes the size of data that can be collected and exploited within the span of a limited time; the approach is very generic and can be adopted to other languages and the ability of the approach to cope with new transliteration phenomena and trends. A statistical transliteration system built using this data improved a comparable system built from Wikipedia wikilinks data.

2015

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How to Avoid Unwanted Pregnancies: Domain Adaptation using Neural Network Models
Shafiq Joty | Hassan Sajjad | Nadir Durrani | Kamla Al-Mannai | Ahmed Abdelali | Stephan Vogel
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Using joint models or domain adaptation in statistical machine translation
Nadir Durrani | Hassan Sajjad | Shafiq Joty | Ahmed Abdelali | Stephan Vogel
Proceedings of Machine Translation Summit XV: Papers

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How do Humans Evaluate Machine Translation
Francisco Guzmán | Ahmed Abdelali | Irina Temnikova | Hassan Sajjad | Stephan Vogel
Proceedings of the Tenth Workshop on Statistical Machine Translation

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QCRI@QALB-2015 Shared Task: Correction of Arabic Text for Native and Non-Native Speakers’ Errors
Hamdy Mubarak | Kareem Darwish | Ahmed Abdelali
Proceedings of the Second Workshop on Arabic Natural Language Processing

2014

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Using Stem-Templates to Improve Arabic POS and Gender/Number Tagging
Kareem Darwish | Ahmed Abdelali | Hamdy Mubarak
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents an end-to-end automatic processing system for Arabic. The system performs: correction of common spelling errors pertaining to different forms of alef, ta marbouta and ha, and alef maqsoura and ya; context sensitive word segmentation into underlying clitics, POS tagging, and gender and number tagging of nouns and adjectives. We introduce the use of stem templates as a feature to improve POS tagging by 0.5\% and to help ascertain the gender and number of nouns and adjectives. For gender and number tagging, we report accuracies that are significantly higher on previously unseen words compared to a state-of-the-art system.

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The AMARA Corpus: Building Parallel Language Resources for the Educational Domain
Ahmed Abdelali | Francisco Guzman | Hassan Sajjad | Stephan Vogel
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languages such as English and Arabic, and resource-poor languages such as Hindi and Thai. In this paper, we describe the gathering, validation, and preprocessing of a large collection of parallel, community-generated subtitles. Furthermore, we describe the methodology used to prepare the data for Machine Translation tasks. Additionally, we provide a document-level, jointly aligned development and test sets for 14 language pairs, designed for tuning and testing Machine Translation systems. We provide baseline results for these tasks, and highlight some of the challenges we face when building machine translation systems for educational content.

2013

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QCRI at IWSLT 2013: experiments in Arabic-English and English-Arabic spoken language translation
Hassan Sajjad | Francisco Guzmán | Preslav Nakov | Ahmed Abdelali | Kenton Murray | Fahad Al Obaidli | Stephan Vogel
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

We describe the Arabic-English and English-Arabic statistical machine translation systems developed by the Qatar Computing Research Institute for the IWSLT’2013 evaluation campaign on spoken language translation. We used one phrase-based and two hierarchical decoders, exploring various settings thereof. We further experimented with three domain adaptation methods, and with various Arabic word segmentation schemes. Combining the output of several systems yielded a gain of up to 3.4 BLEU points over the baseline. Here we also describe a specialized normalization scheme for evaluating Arabic output, which was adopted for the IWSLT’2013 evaluation campaign.

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The AMARA corpus: building resources for translating the web’s educational content
Francisco Guzman | Hassan Sajjad | Stephan Vogel | Ahmed Abdelali
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

In this paper, we introduce a new parallel corpus of subtitles of educational videos: the AMARA corpus for online educational content. We crawl a multilingual collection community generated subtitles, and present the results of processing the Arabic–English portion of the data, which yields a parallel corpus of about 2.6M Arabic and 3.9M English words. We explore different approaches to align the segments, and extrinsically evaluate the resulting parallel corpus on the standard TED-talks tst-2010. We observe that the data can be successfully used for this task, and also observe an absolute improvement of 1.6 BLEU when it is used in combination with TED data. Finally, we analyze some of the specific challenges when translating the educational content.

2009

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Investigations on Standard Arabic Geographical Classification
Ahmed Abdelali | Steve Helmreich | Ron Zacharski
Proceedings of the Third Workshop on Computational Approaches to Arabic-Script-based Languages (CAASL3)

2008

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The Effects of Language Relatedness on Multilingual Information Retrieval: A Case Study With Indo-European and Semitic Languages
Peter Chew | Ahmed Abdelali
Proceedings of the 2nd workshop on Cross Lingual Information Access (CLIA) Addressing the Information Need of Multilingual Societies

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Latent Morpho-Semantic Analysis: Multilingual Information Retrieval with Character N-Grams and Mutual Information
Peter A. Chew | Brett W. Bader | Ahmed Abdelali
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Benefits of the ‘Massively Parallel Rosetta Stone’: Cross-Language Information Retrieval with over 30 Languages
Peter Chew | Ahmed Abdelali
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Guarani: A Case Study in Resource Development for Quick Ramp-Up MT
Ahmed Abdelali | James Cowie | Steve Helmreich | Wanying Jin | Maria Pilar Milagros | Bill Ogden | Mansouri Rad | Ron Zacharski
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers

In this paper we describe a set of processes for the acquisition of re­sources for quick ramp­up machine translation (MT) from any language lacking significant machine tracta­ble resources into English, using the Paraguayan indigenous lan­guage Guarani as well as Amharic and Chechen, as examples. Our task is to develop a 250,000 mono­lingual corpus, a 250,000 bilingual parallel corpus, and smaller corpora tagged with part of speech, named entity, and morphological annota­tions.
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