The goal of the paper is to predict answers to questions given a passage of Qur'an. The answers are always found in the passage, so the task of the model is to predict where an answer starts and where it ends. As the initial data set is rather small for training, we make use of multilingual BERT so that we can augment the training data by using data available for languages other than Arabic. Furthermore, we crawl a large Arabic corpus that is domain specific to religious discourse. Our approach consists of two steps, first we train a BERT model to predict a set of possible answers in a passage. Finally, we use another BERT based model to rank the candidate answers produced by the first BERT model.
IBM Cairo Human Language Technologies Group（IBM开罗人类语言技术组）
Social media data in Arabic language is becoming more and more abundant. It is a consensus that valuable information lies in social media data. Mining this data and making the process easier are gaining momentum in the industries. This paper describes an enterprise system we developed for extracting sentiment from large volumes of social data in Arabic dialects. First, we give an overview of the Big Data system for information extraction from multilingual social data from a variety of sources. Then, we focus on the Arabic sentiment analysis capability that was built on top of the system includ…
Ohio State University（俄亥俄州立大学）；Georgia Institute of Technology（佐治亚理工学院）
Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the effective cross-lingual zero-shot transfer. However, their performance on Arabic information extraction (IE) tasks is not very well studied. In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. We study GigaBERT's effectiveness on zero-short transfer across four IE tasks: named entity recognition, part-of-speech tagging, argument role …
MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark
Engineering Division, New York University Abu Dhabi（纽约大学阿布扎克分校工程系）；Cleveland Clinic Abu Dhabi（阿布扎克克利夫兰诊所）；Science Division, New York University Abu Dhabi（纽约大学阿布扎克分校科学系）
The Arabic language is a morphologically rich language with relatively few resources and a less explored syntax compared to English. Given these limitations, Arabic Natural Language Processing (NLP) tasks like Sentiment Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA), have proven to be very challenging to tackle. Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus. Such models were able to set new standards and achiev…
We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers. We run baselines over Mintaka, the best of which achieves 38% hits@1 i…
AraHealthQA 2025: The First Shared Task on Arabic Health Question Answering
Umm Al-Qura University（乌姆·阿勒·库拉大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；The University of British Columbia（不列颠哥伦比亚大学）；King Khalid University（国王 Khalid 大学）
NEC Laboratories Europe（NEC欧洲实验室）；Heidelberg University（海德堡大学）；University of Stuttgart（斯图加特大学）
Open Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences. Current OpenIE systems extract all triple slots independently. In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction. Based on this hypothesis, we propose a neural OpenIE system, milIE, that operates in an iterative fashion. Due to the iterative nature, the system is also modu…
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；INSAIT, Sofia University “St. Kliment Ohridski”（索非亚大学“圣克莱门特·奥赫里德斯基”INSAIT学院）；University of Arizona（亚利桑那大学）；FMI, Sofia University “St. Kliment Ohridski”（索非亚大学“圣克莱门特·奥赫里德斯基”FMI学院）；The Fin AI（The Fin AI公司）；The University of Tokyo（东京大学）；Linköping University（林雪平大学）；McGill University（麦吉尔大学）；Mila, Quebec AI Institute（魁北克Mila人工智能研究所）；Meiji Gakuin University（明治学院大学）
Baidu（百度）；Shanghai Pudong Development Bank（上海浦东发展银行）
Detecting sarcasm and verbal irony from people's subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios. This paper describes the X-PuDu system that participated in SemEval-2022 Task 6, iSarcasmEval - Intended Sarcasm Detection in English and Arabic, which aims at detecting intended sarcasm in various settings of natural language understanding. Our solution finetunes pre-trained language models, such as ERNIE-M and DeBERTa, under the multilingual settings to recognize the irony from Arabic and English texts. Our system ra…
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；IBM Research AI（IBM研究院人工智能部门）；Qassim University（卡西姆大学）
In multilingual question answering, either the question needs to be translated into the document language, or vice versa. In addition to direction, there are multiple methods to perform the translation, four of which we explore in this paper: word-based, 10-best, context-based, and grammar-based. We build a feature for each combination of translation direction and method, and train a model that learns optimal feature weights. On a large forum dataset consisting of posts in English, Arabic, and Chinese, our novel learn-to-translate approach was more effective than a strong baseline (p<0.05): tr…
As natural language processing systems become more widespread, it is necessary to address fairness issues in their implementation and deployment to ensure that their negative impacts on society are understood and minimized. However, there is limited work that studies fairness using a multilingual and intersectional framework or on downstream tasks. In this paper, we introduce four multilingual Equity Evaluation Corpora, supplementary test sets designed to measure social biases, and a novel statistical framework for studying unisectional and intersectional social biases in natural language proc…
Searching for an available, reliable, official, and understandable information is not a trivial task due to scattered information across the internet, and the availability lack of governmental communication channels communicating with African dialects and languages. In this paper, we introduce an Artificial Intelligence Powered chatbot for crisis communication that would be omnichannel, multilingual and multi dialectal. We present our work on modified StarSpace embedding tailored for African dialects for the question-answering task along with the architecture of the proposed chatbot system and…
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus
New York University Abu Dhabi（纽约大学阿布扎比分校）；University of Stuttgart（斯图加特大学）；University of Tübingen（蒂宾根大学）；United Arab Emirates University（阿联酋大学）；Zayed University（扎耶德大学）
AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs
Information and Computer Science Department, King Fahd University of Petroleum and Minerals（信息与计算机科学系，国王法赫德石油和矿物大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）
Johns Hopkins University Human Language Technology Center of Excellence（约翰霍普金斯大学人机语言技术卓越中心）；University of Glasgow（格拉斯哥大学）；Allen Institute for AI（人工智能研究院）
The Role of Orthographic Consistency in Multilingual Embedding Models for Text Classification in Arabic-Script Languages
University of Kurdistan Hewler（库尔德斯坦赫勒大学）；Soran University（索兰大学）；University of Technology Sydney（悉尼科技大学）；Torrens University Australia（澳大利亚托伦斯大学）；Sunway University Malaysia（马来西亚双威大学）；University of Kragujevac（克拉古耶瓦茨大学）；University of Tehran（德黑兰大学）
ReceiptSense: Beyond Traditional OCR -- A Dataset for Receipt Understanding
Innsbruck University（因斯布鲁克大学）；High Institute for Computers（计算机与管理信息高级学院）；Chungbuk National University（Chungbuk国立大学）；Louisville University（路易斯维尔大学）；University of Innsbruck（因斯布鲁克大学）
This paper presents our system entitled `LIIR' for SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2). We have participated in sub-task A for English, Danish, Greek, Arabic, and Turkish languages. We adapt and fine-tune the BERT and Multilingual Bert models made available by Google AI for English and non-English languages respectively. For the English language, we use a combination of two fine-tuned BERT models. For other languages we propose a cross-lingual augmentation approach in order to enrich training data and we use Multilingual BERT to…
Qatar Computing Research Institute（卡塔尔计算研究所）；KanarI AI
With the advent of globalization, there is an increasing demand for multilingual automatic speech recognition (ASR), handling language and dialectal variation of spoken content. Recent studies show its efficacy over monolingual systems. In this study, we design a large multilingual end-to-end ASR using self-attention based conformer architecture. We trained the system using Arabic (Ar), English (En) and French (Fr) languages. We evaluate the system performance handling: (i) monolingual (Ar, En and Fr); (ii) multi-dialectal (Modern Standard Arabic, along with dialectal variation such as Egyptia…
Virtual Adversarial Training (VAT) has been effective in learning robust models under supervised and semi-supervised settings for both computer vision and NLP tasks. However, the efficacy of VAT for multilingual and multilabel text classification has not been explored before. In this work, we explore VAT for multilabel emotion recognition with a focus on leveraging unlabelled data from different languages to improve the model performance. We perform extensive semi-supervised experiments on SemEval2018 multilabel and multilingual emotion recognition dataset and show performance gains of 6.2% (A…
Building natural language processing systems for non standardized and low resource languages is a difficult challenge. The recent success of large-scale multilingual pretrained language models provides new modeling tools to tackle this. In this work, we study the ability of multilingual language models to process an unseen dialect. We take user generated North-African Arabic as our case study, a resource-poor dialectal variety of Arabic with frequent code-mixing with French and written in Arabizi, a non-standardized transliteration of Arabic to Latin script. Focusing on two tasks, part-of-spee…
The use of multilingual language models for tasks in low and high-resource languages has been a success story in deep learning. In recent times, Arabic has been receiving widespread attention on account of its dialectal variance. While prior research studies have tried to adapt these multilingual models for dialectal variants of Arabic, it still remains a challenging problem owing to the lack of sufficient monolingual dialectal data and parallel translation data of such dialectal variants. It remains an open problem on whether the limited dialectical data can be used to improve the models trai…
Unsupervised translation has reached impressive performance on resource-rich language pairs such as English-French and English-German. However, early studies have shown that in more realistic settings involving low-resource, rare languages, unsupervised translation performs poorly, achieving less than 3.0 BLEU. In this work, we show that multilinguality is critical to making unsupervised systems practical for low-resource settings. In particular, we present a single model for 5 low-resource languages (Gujarati, Kazakh, Nepali, Sinhala, and Turkish) to and from English directions, which leverag…
Most of previous work on learning diacritization of the Arabic language relied on training models from scratch. In this paper, we investigate how to leverage pre-trained language models to learn diacritization. We finetune token-free pre-trained multilingual models (ByT5) to learn to predict and insert missing diacritics in Arabic text, a complex task that requires understanding the sentence semantics and the morphological structure of the tokens. We show that we can achieve state-of-the-art on the diacritization task with minimal amount of training and no feature engineering, reducing WER by …
Google Translate Error Analysis for Mental Healthcare Information: Evaluating Accuracy, Comprehensibility, and Implications for Multilingual Healthcare Communication
University of Surrey（萨里大学）；University of Amsterdam（阿姆斯特丹大学）
Jordan University of Science and Technology（约旦科技大学）
This paper describes our method for the task of Semantic Question Similarity in Arabic in the workshop on NLP Solutions for Under-Resourced Languages (NSURL). The aim is to build a model that is able to detect similar semantic questions in the Arabic language for the provided dataset. Different methods of determining questions similarity are explored in this work. The proposed models achieved high F1-scores, which range from (88% to 96%). Our official best result is produced from the ensemble model of using a pre-trained multilingual BERT model with different random seeds with 95.924% F1-Score…
Language-specific pre-trained models have proven to be more accurate than multilingual ones in a monolingual evaluation setting, Arabic is no exception. However, we found that previously released Arabic BERT models were significantly under-trained. In this technical report, we present JABER and SABER, Junior and Senior Arabic BERt respectively, our pre-trained language model prototypes dedicated for Arabic. We conduct an empirical study to systematically evaluate the performance of models across a diverse set of existing Arabic NLU tasks. Experimental results show that JABER and SABER achieve …
The George Washington University（乔治华盛顿大学）；AWS, Amazon AI（亚马逊云科技亚马逊人工智能）
Linguistic Code Switching (CS) is a phenomenon that occurs when multilingual speakers alternate between two or more languages/dialects within a single conversation. Processing CS data is especially challenging in intra-sentential data given state-of-the-art monolingual NLP technologies since such technologies are geared toward the processing of one language at a time. In this paper, we address the problem of Part-of-Speech tagging (POS) in the context of linguistic code switching (CS). We explore leveraging multiple neural network architectures to measure the impact of different pre-trained em…
University of Leeds（利兹大学）；Alexandria University（亚历山大大学）
This paper presents an attempt to build a Modern Standard Arabic (MSA) sentence-level simplification system. We experimented with sentence simplification using two approaches: (i) a classification approach leading to lexical simplification pipelines which use Arabic-BERT, a pre-trained contextualised model, as well as a model of fastText word embeddings; and (ii) a generative approach, a Seq2Seq technique by applying a multilingual Text-to-Text Transfer Transformer mT5. We developed our training corpus by aligning the original and simplified sentences from the internationally acclaimed Arabic …
Department of Electrical and Electronics Engineering（电气与电子工程系）
Fine-tuning a pretrained BERT model is the state of the art method for extractive/abstractive text summarization, in this paper we showcase how this fine-tuning method can be applied to the Arabic language to both construct the first documented model for abstractive Arabic text summarization and show its performance in Arabic extractive summarization. Our model works with multilingual BERT (as Arabic language does not have a pretrained BERT of its own). We show its performance in English corpus first before applying it to Arabic corpora in both extractive and abstractive tasks.
School of Interactive Computing（交互计算学院）；Georgia Institute of Technology（佐治亚理工学院）
We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims. As far as we are aware, it is the largest corpus annotated with stance towards misinformation claims. The claims in Stanceosaurus originate from 15 fact-checking sources that cover diverse geographical regions and cultures. Unlike existing stance datasets, we introduce a more fine-grained 5-class labeling strategy with additional subcategories to distinguish implicit stance. Pre-trained transformer-based stance classifiers that are fine-tuned on our cor…
ICC, CONICET（阿根廷国家科学与技术研究理事会计算机与通信研究所）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）；University of Chile（智利大学）；IMFD, Chile（智利千禧数据基金会）；CONICET（阿根廷国家科学与技术研究理事会）；Universidad Nacional de Córdoba（国立科尔多瓦大学）
This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research
QU-NLP at CheckThat! 2025: Multilingual Subjectivity in News Articles Detection using Feature-Augmented Transformer Models with Sequential Cross-Lingual Fine-Tuning
