Automatic readability assessment is relevant to building NLP applications for education, content analysis, and accessibility. However, Arabic readability assessment is a challenging task due to Arabic's morphological richness and limited readability resources. In this paper, we present a set of experimental results on Arabic readability assessment using a diverse range of approaches, from rule-based methods to Arabic pretrained language models. We report our results on a newly created corpus at different textual granularity levels (words and sentence fragments). Our results show that combining different techniques yields the best results, achieving an overall macro F1 score of 86.7 at the word level and 87.9 at the fragment level on a blind test set. We make our code, data, and pretrained models publicly available.
This demo paper presents a Google Docs add-on for automatic Arabic word-level readability visualization. The add-on includes a lemmatization component that is connected to a five-level readability lexicon and Arabic WordNet-based substitution suggestions. The add-on can be used for assessing the reading difficulty of a text and identifying difficult words as part of the task of manual text simplification. We make our add-on and its code publicly available.
A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment
Computational Approaches to Modeling Language Lab, New York University Abu Dhabi（新纽约大学阿布扎赫实验室）；Zai Arabic Language Research Centre, Zayed University（扎伊阿拉伯语言研究中心）
Guidelines for Fine-grained Sentence-level Arabic Readability Annotation
Computational Approaches to Modeling Language Lab, New York University Abu Dhabi（纽约大学阿布扎克分校计算语言学建模实验室）；Zai Arabic Language Research Centre, Zayed University（扎伊阿拉伯语言研究中心，扎耶德大学）；Abu Dhabi Arabic Language Centre（阿布扎克阿拉伯语言中心）
University of Ferhat Abbas Sétif 1（费尔哈特·阿巴斯·塞蒂夫第一大学）
This paper presents an approach based on supervised machine learning methods to build a classifier that can identify text complexity in order to present Arabic language learners with texts suitable to their levels. The approach is based on machine learning classification methods to discriminate between the different levels of difficulty in reading and understanding a text. Several models were trained on a large corpus mined from online Arabic websites and manually annotated. The model uses both Count and TF-IDF representations and applies five machine learning algorithms; Multinomial Naive Bay…
RephQA: Evaluating Readability of Large Language Models in Public Health Question Answering
Yale University, Department of Computer Science（耶鲁大学计算机科学系）；Yale University, School of Nursing（耶鲁大学护理学院）；Yale University, School of Public Health（耶鲁大学公共卫生学院）；Northeastern University（东北大学）
AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP
King Fahd University of Petroleum and Minerals（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）；Mohammed VI Polytechnic University（穆莱·易卜拉欣(polytechnic)大学）
University of Washington（华盛顿大学）；SRI International（SRI国际研究所）；LinkedIn（领英）
Statistical machine translation for dialectal Arabic is characterized by a lack of data since data acquisition involves the transcription and translation of spoken language. In this study we develop techniques for extracting parallel data for one particular dialect of Arabic (Iraqi Arabic) from out-of-domain corpora in different dialects of Arabic or in Modern Standard Arabic. We compare two different data selection strategies (cross-entropy based and submodular selection) and demonstrate that a very small but highly targeted amount of found data can improve the performance of a baseline machi…
Language-Specific versus Cross-Lingual Knowledge Graphs for Implicit Aspect Identification in Arabic: A Comparative Study of Reasoning and Adaptation Strategies
UM_FHS at TREC 2024 PLABA: Exploration of Fine-tuning and AI agent approach for plain language adaptations of biomedical text
University of Maribor（马里博尔大学）；University of Ljubljana（卢布尔雅那大学）；Columbia University（哥伦比亚大学）；Manisa Celal Bayar University（马尼萨杰拉尔·巴亚尔大学）；University of Edinburgh（爱丁堡大学）
Native vs Non-Native Language Prompting: A Comparative Analysis
Qatar Computing Research Institute（卡塔尔计算研究所）；Qatar University（卡塔尔大学）；Qatar University of Doha for Science and Technology（多哈科学技术卡塔尔大学）；Liverpool John Moores University（利物浦约翰摩尔大学）；Carnegie Mellon University in Qatar（卡塔尔卡内基梅隆大学）
The George Washington University（乔治·华盛顿大学）；Princess Nourah Bint Abdul Rahman University（诺拉·宾特·阿卜杜勒拉赫曼公主大学）；AWS, Amazon AI（亚马逊人工智能服务（AWS））
Lexical ambiguity, a challenging phenomenon in all natural languages, is particularly prevalent for languages with diacritics that tend to be omitted in writing, such as Arabic. Omitting diacritics leads to an increase in the number of homographs: different words with the same spelling. Diacritic restoration could theoretically help disambiguate these words, but in practice, the increase in overall sparsity leads to performance degradation in NLP applications. In this paper, we propose approaches for automatically marking a subset of words for diacritic restoration, which leads to selective ho…
Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziza University, Jeddah, Saudi Arabia（计算机科学系，计算与信息技术学院，国王阿卜杜勒阿齐兹大学，沙特阿拉伯吉达）
