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The aim of this study was to examine whether, when a person reads, there is a boost to all material related to the context in Najdi Arabic (NA) as was found in English in was found in Rodd et al. (2013). The study employs Arabic language features of orthographic style and diglossia to answer the research question.

Forty-two participants were asked to come on two sessions to complete a reading task, a filler task and a word recognition task. The word recognition task included 14 homographs that could mean one thing in Modern Standard Arabic and another in NA.

The findings show that the assumption that all related materials are ready to be used when readers are exposed to the context is not valid for the two Arabic variants.

One limitation of this study was that the participants were all female.

The findings could help writers write better texts to help individuals who struggle with reading comprehension whether it is because of dyslexia, Attention Deficit Disorder (ADD) or Attention Deficit Hyperactivity Disorder (ADHD) as when researchers understand how priming works, they might be able to help readers in their reading fluency and comprehension (Rodd et al., 2016). This could be by producing better texts to comprehend or using semantic priming in classroom setups.

The paper aims to enhance Arabic machine translation (MT) by proposing novel approaches: (1) a dimensionality reduction technique for word embeddings tailored for Arabic text, optimizing efficiency while retaining semantic information; (2) a comprehensive comparison of meta-embedding techniques to improve translation quality; and (3) a method leveraging self-attention and Gated CNNs to capture token dependencies, including temporal and hierarchical features within sentences, and interactions between different embedding types. These approaches collectively aim to enhance translation quality by combining different embedding schemes and leveraging advanced modeling techniques.

Recent works on MT in general and Arabic MT in particular often pick one type of word embedding model. In this paper, we present a novel approach to enhance Arabic MT by addressing three key aspects. Firstly, we propose a new dimensionality reduction technique for word embeddings, specifically tailored for Arabic text. This technique optimizes the efficiency of embeddings while retaining their semantic information. Secondly, we conduct an extensive comparison of different meta-embedding techniques, exploring the combination of static and contextual embeddings. Through this analysis, we identify the most effective approach to improve translation quality. Lastly, we introduce a novel method that leverages self-attention and Gated convolutional neural networks (CNNs) to capture token dependencies, including temporal and hierarchical features within sentences, as well as interactions between different types of embeddings. Our experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing Arabic MT performance. It outperforms baseline models with a BLEU score increase of 2 points and achieves superior results compared to state-of-the-art approaches, with an average improvement of 4.6 points across all evaluation metrics.

The proposed approaches significantly enhance Arabic MT performance. The dimensionality reduction technique improves the efficiency of word embeddings while preserving semantic information. Comprehensive comparison identifies effective meta-embedding techniques, with the contextualized dynamic meta-embeddings (CDME) model showcasing competitive results. Integration of Gated CNNs with the transformer model surpasses baseline performance, leveraging both architectures' strengths. Overall, these findings demonstrate substantial improvements in translation quality, with a BLEU score increase of 2 points and an average improvement of 4.6 points across all evaluation metrics, outperforming state-of-the-art approaches.

The paper’s originality lies in its departure from simply fine-tuning the transformer model for a specific task. Instead, it introduces modifications to the internal architecture of the transformer, integrating Gated CNNs to enhance translation performance. This departure from traditional fine-tuning approaches demonstrates a novel perspective on model enhancement, offering unique insights into improving translation quality without solely relying on pre-existing architectures. The originality in dimensionality reduction lies in the tailored approach for Arabic text. While dimensionality reduction techniques are not new, the paper introduces a specific method optimized for Arabic word embeddings. By employing independent component analysis (ICA) and a post-processing method, the paper effectively reduces the dimensionality of word embeddings while preserving semantic information which has not been investigated before especially for MT task.

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.

Assessing whether a book is relevant or suitable for use in teaching materials is not an easy and haphazard matter, various methods and theories have been offered by researchers in studying this matter. Taking a study of the context of textbooks, researchers found the urgency that textbooks are a foundation for education, socialization and transmission of knowledge and its construction. Researchers offer another approach, namely by using praxeology as a study tool so that the goals of the textbooks previously intended are fulfilled.

The researcher uses a qualitative approach through grounded theory. Grounded theory procedures are designed to develop a well-integrated set of concepts that provide a thorough theoretical explanation of the social phenomena under study. A grounded theory must explain as well as describe. It may also implicitly provide some degree of predictability, but only with respect to certain conditions (Corbin and Strauss, 1990). Document analysis in conducting this research study. Document analysis itself examines systematic procedures for reviewing or evaluating documents, both printed and electronic materials.

Two issues regarding gender acquisition have been investigated in L2 Arabic acquisition studies; the order in which L2 Arabic learners acquire certain grammatical features of the gender system and the effect of L1 on the acquisition of some grammatical features from L2 grammatical gender. Arabic has a two-gender system that classifies all nouns, animate and inanimate, as masculine or feminine. Verbs, nouns, adjectives, personal, demonstrative and relative pronouns related to nouns in the syntactic structure of sentences show gender agreement.

In practice, as a book intended for non-speakers, the book is presented using a general view of linguistic theory. In relation to the gender agreement, the presentation of the book begins and is inserted with the concepts of nouns and verbs. Returning to the praxeology context, First, The Know How (Praxis) explains practice (i.e. the tasks performed and the techniques used). Second, To Know Why or Knowledge (logos) which explains and justifies practice from a technological and theoretical point of view. Answering the first concept, the exercise presented in the book is a concept with three clusters explained at the beginning of the discussion. And the second concept, explained with a task design approach which includes word categorization by separating masculine and feminine word forms.

Practically, this research obtains perspectives studied from a textbook, namely the Arabic gender agreement is presented with various examples of noun contexts; textbook authors present book concepts in a particular way with regard to curriculum features and this task design affects student performance, and which approach is more effective for developing student understanding. Empirically, the material is in line with the formulation of competency standards for non-Arabic speakers in Indonesia.

With this computational search, the researcher found a novelty that was considered accurate by taking the praxeology context as a review in the analysis of non-speaking Arabic textbooks, especially in the year 2022 (last data collection in September) there has been no study on this context. So then, the researcher finds other interests in that praxeology can examine more broadly parts of the task of the contents of the book with the approach of relevant linguistic theories.

This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.

Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.

The findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.

At the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.

We have developed in this paper a morphological disambiguation hybrid system for the Arabic language that identifies the stem, lemma and root of a given sentence words. Following an out-of-context analysis performed by the morphological analyser Alkhalil Morpho Sys, the system first identifies all the potential tags of each word of the sentence. Then, a disambiguation phase is carried out to choose for each word the right solution among those obtained during the first phase. This problem has been solved by equating the disambiguation issue with a surface optimization problem of spline functions. Tests have shown the interest of this approach and the superiority of its performances compared to those of the state of the art.

Audio-visual translation (AVT) is recognized as the most vibrant type of translation. While AVT plays a vital function in the field of translation, its significance within cultural studies hasn’t been thoroughly investigated. This research aims to uncover the predominant techniques employed in translating idiomatic expressions found in subtitled movies from English to Arabic.

The corpus utilized in the current study consisted of five movies. The dialog in the chosen English films was examined to identify idiomatic expressions. The occurrence and proportion of each approach employed in translating English idioms into Arabic were calculated.

The findings of this investigation unveiled that the most common technique employed by translators to render idioms was retaining both similar meaning and similar form. The second approach involved maintaining a similar meaning while altering the form. The third method involved paraphrasing for translation. The fourth strategy was compensation-based translation. When dealing with verb and object idioms as well as similes, the predominant techniques were retaining both similar meaning and similar form, followed by a similar meaning but different form approach. Compound idioms were translated using paraphrasing, similar meaning but different form and similar meaning but different form strategies.

The research was constrained to examining the utilization of subtitles for the translation of five films. The films and corresponding Arabic subtitles were obtained and saved in plain text formats. Solely idiomatic expressions were scrutinized to determine the translation approaches employed through the use of subtitles.

English as a foreign langauge (EFL) students find it difficult to apply the theoretical knowledge they acquire on translation in the practical world. Therefore, this study explored if training in pretranslation techniques (PTTs) (syntactic parsing) as suggested by Almanna (2018) could improve the translation proficiency of Yemeni EFL students. Moreover, the study also assessed which of the PTTs the intervention helped to develop.

The study adopted a primarily experimental pre- and posttests research design, and the sample comprised of an intake class with 16 students enrolled in the fourth year, Bachelor in Education (B.Ed), Hadhramout University. Six participants were also interviewed to gather the students' perceptions on using PTTs.

Results showed that students' performance in translation developed significantly (Sig. = 0.002). All the six PTTs showed development, though subject, tense and aspect developed more significantly (Sig. = 0.034, 0.002, 0.001 respectively). Finally, the study reported students' positive perceptions on the importance of using PTTs before doing any translation tasks.

One of the recurrent errors that can be noticed in Yemeni EFL students' production is their inability to transfer the grammatical elements of sentences from L1 (Arabic) into L2 (English) or the visa versa. The researchers thought though translation is more than the syntactic transmission of one language into another, analyzing the elements of sentences using syntactic and semantic parsing can help students to produce acceptable texts in the target language. These claims would be proved or refuted after analyzing the experiment result of the present study.

This research explores the potential incorporation of Indonesian folklore, particularly the folklore of the origin of Bandung, into Arabic language teaching materials. Integrating local cultural elements into language instruction can enhance students' language learning experience and promote a deeper understanding of Indonesian culture.

The origin city of Bandung is one of the famous folklore stories in Indonesia that originates from West Java. Through ethnographic studies, various aspects of the folklore of the origin of the city of Bandung will be examined, which can be utilized in Arabic language teaching. This includes analyzing the linguistic structure, vocabulary and cultural references embedded in the story. Additionally, we will explore how teachers can effectively incorporate this folklore into their teaching materials to create engaging and culturally relevant Arabic language lessons.

This research aims to contribute to developing innovative and culturally responsive language learning by investigating the use of Indonesian folklore in Arabic language instruction. It aims to empower teachers and students by fostering a deeper appreciation of Indonesian culture while improving Arabic language proficiency. This approach enhances language learning outcomes, promotes cultural awareness and cultivates a deeper connection between students and the local culture. By embracing folklore, teachers can create dynamic and meaningful language learning experiences that empower students to become proficient Arabic speakers with a strong appreciation for Indonesian wisdom and culture.

Overall, using folklore as a foreign language teaching material provides significant advantages for students in developing a deeper understanding of language, cultural skills and interest in the language and culture being studied. This approach improves language learning outcomes, promotes cultural awareness and fosters deeper relationships between students and local culture so that foreign language (Arabic) learning is effective. By embracing folklore, teachers can create dynamic and meaningful language learning experiences that empower students to become proficient Arabic speakers with a strong appreciation for local Indonesian wisdom and culture.

Practically, this involves understanding the social, political, economic or cultural context in which the folklore arose, as well as new interpretations that provide a broader understanding of the stories' values, messages or conflicts. In contributing to knowledge, this research has implications for insight into linguistic knowledge and learning a second language in the norms of the first language without shifting the culture of the first language.

Empirically, this study combines an interdisciplinary approach to folklore research that can bring a new understanding of the relationship between folklore and other fields such as linguistics, literature, anthropology or psychology. This approach can produce a more comprehensive insight into folklore and its influence on various aspects of life. Contributing to social impact, introducing culture in learning materials is an educational tourism attraction for students. This also has an impact on public policy that tourism education about folklore is introduced as teaching and used as further field study for students.

This research conceptualizes the diversity of Indonesian culture integrated into the instructional materials of Arabic language learning in Indonesia through folklore. To integrate folktales into the learning process, families, traditions and rituals need to play a role in preserving and educating. Researchers can develop new insights, broaden our understanding of culture and traditions and enrich our knowledge and cultural heritage. These new approaches have the potential to yield discoveries and a deeper understanding of folklore as an important cultural heritage. This research explores the potential incorporation of Indonesian folklore, particularly the folklore of the origin of Bandung, into Arabic language teaching materials. Integrating local cultural elements into Arabic language teaching for Indonesian speakers can enhance students' language learning experience and promote a deeper understanding of Indonesian culture through Arabic language teaching as a foreign language.

The aim of this study is to offer valuable insights to businesses and facilitate better understanding on transformer-based models (TBMs), which are among the widely employed generative artificial intelligence (GAI) models, garnering substantial attention due to their ability to process and generate complex data.

Existing studies on TBMs tend to be limited in scope, either focusing on specific fields or being highly technical. To bridge this gap, this study conducts robust bibliometric analysis to explore the trends across journals, authors, affiliations, countries and research trajectories using science mapping techniques – co-citation, co-words and strategic diagram analysis.

Identified research gaps encompass the evolution of new closed and open-source TBMs; limited exploration across industries like education and disciplines like marketing; a lack of in-depth exploration on TBMs' adoption in the health sector; scarcity of research on TBMs' ethical considerations and potential TBMs' performance research in diverse applications, like image processing.

The study offers an updated TBMs landscape and proposes a theoretical framework for TBMs' adoption in organizations. Implications for managers and researchers along with suggested research questions to guide future investigations are provided.