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Neural machine translation (NMT) is a complex process that deals with many grammatical complexities. Today, transfer learning (TL) has emerged as a leading method in machine translation, enhancing accuracy with ample source data for limited target data. Yet, low-resource languages such as Arabic dialects lack substantial source data. This study aims to enable an NMT model, trained on a sparse Arabic dialect corpus, to translate a precise dialect with a limited corpus, addressing this gap. This paper introduces an incremental transfer learning approach tailored for translating low-resource language. The method utilizes various related language corpora, employing an incremental fine-tuning strategy to transfer linguistic features from a grand-parent model to a child model. In our case, Knowledge is transferred from a broad set of Arabic dialects to the Maghrebi dialects subset and then to specific low-resource dialects such as Algerian, Tunisian, and Moroccan, employing Transformer and attentional sequence-to-sequence models. The evaluation of the proposed strategy on Algerian, Tunisian, and Moroccan dialects demonstrates superior translation performance compared to traditional TL methods. Using the Transformer model, it shows improvements of 80%, 62%, and 58% for Algerian, Tunisian, and Moroccan dialects, respectively. Similarly, with the Attentional seq2seq model, there's an enhancement of 98% in BLEU score results for Algerian, Tunisian, and Moroccan dialects. [ABSTRACT FROM AUTHOR]

The present study investigates invitation speech act strategies employed by Iraqi Arabs in the Iraqi Arabic dialect. A modified version of an open-ended Written Discourse Completion Task was used to collect data from three situations in which the participants were to invite lower-status interlocutors with close, familiar, and distant social distance. Based on Garca's (2008) classification of invitation strategies as head acts and supportive moves, the obtained data were analyzed descriptively. The study reveals that Iraqi Arabs employed 25 types of invitation strategies, which is 12 more than those used in García's (2008) categorization. Head acts were the most preferred strategies used by Iraqi Arabs compared to supportive moves. Iraqi Arabs preferred directness rather than indirectness in making their invitations, showing that they viewed invitations as face-enhancing acts rather than face-threatening acts. Direct strategies were the most dominant among head acts, whereas aggravators from supportive moves were the most utilized ones. It has also been shown that mood derivable (imperative) as a direct strategy, which ranked first on the directness scale, was the most preferred strategy in making invitations across the three levels of social distance, i.e., close, familiar, and distant. Additionally, complimenting as a supportive move was the most frequently employed strategy across the three situations. Some new strategies, which were neither included in García's (2008) classification nor in the results of other studies, were employed by Iraqi Arabs, such as showing solidarity, addressing terms, indebting by the inviter, requesting a promise, rejecting excuses, warning, insisting, and invoking the name of God. This study will provide non-Iraqi Arabic speakers, who belong to different ethnicities and cultures and speak different languages, with pragmatic knowledge when making invitations in any possible communication with Iraqi Arabs, and consequently, pragmatic failures and misunderstandings will be avoided. Previous studies focused on social status, while social distance has not been given its due importance. The present study focuses on the three levels of social distance. [ABSTRACT FROM AUTHOR]

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We present a cross-domain and multi-dialectal stance corpus for Arabic, covering the major dialect groups and four Arab regions. This research provides an important language resource for automating the task of stance detection in Dialectal Arabic while carefully considering the subtle differences in stance expression across various dialects. More than 4500 sentences in our corpus have been carefully annotated according to their stance with regard to a certain subject. We gathered sentences associated with two controversial topics for every region and we had at least two annotators annotate each sentence to indicate if the author is supporting, opposing, or neutral to the sentence's topic. Our corpus shows high balance between dialect and stance. About half of the sentences in each region are written in Modern Standard Arabic, while the other half are written in the specific dialect of that region. To evaluate our corpus, we performed a number of machine-learning experiments for the stance detection task. The best performance was achieved by AraBERT with an accuracy and an F1-score of 0.82. Furthermore, we trained and tested this model on the most similar state-of-the-art stance dataset, "MAWQIF". The comparison results demonstrate how crucial it is to maintain balance among the three stance classes in our dataset. In particular, the model scored better when using our stance corpus than when using the MAWQIF dataset especially for the "Neutral" stance class. Using our best performing model, we developed a Web-based demonstrator for stance detection in dialectal Arabic and we show its effectiveness in analyzing stance in the context of two real-world scenarios: product boycott in the Arab world and customer reviews of a soft drink company. [ABSTRACT FROM AUTHOR]

Teknonyms represent a special category of the addressing system of the Arabic language and most of its dialects. The present study analyzes Syrian Arabic teknonyms that have not been tackled before to highlight how often, in what situational contexts they are used and their pragmatic characteristics. The data were elicited from open-ended questionnaires supplemented by interviews with 74 representatives of the Syrian lingua-culture of both genders and subjected to a socio-pragmatic and lingua-cultural analysis. The findings show that teknonyms are an inherent feature of the modern Syrian culture and a significant identity characteristic of its representatives used as a marker of politeness. The results indicate that teknonyms pragmatically afford speakers the opportunity to simultaneously achieve closeness and respect. • Teknonymy is the practice of addressing a parent by the name of his/her children. • The choice of a teknonym complies with the norms of sociolinguistics. • Teknonyms are sensitive to the interlocutor's age and gender. • Teknonyms can serve as markers of distance as well as intimacy/solidarity. • Teknonyms can be used for both negative and positive politeness strategies. [ABSTRACT FROM AUTHOR]

In recent years, social media has witnessed an exponential growth in promoting healthy relationships and communication between family, friends, and acquaintances, but it isn't without its flaws. It is clear that sometimes social media freedom can create an unattractive online environment. Hate speech and offensive language are frequently spread on social media platforms. Thus, they encompass different negative effects on our society. Therefore, detecting hate speech and offensive language has become the theme of one of the major research trends. Although the Arabic language occupies a distinct position among the languages on social media networks such as Twitter and Facebook, the ability to identify Arabic hate speech and offensive language is still developing due to the variety and complexity of Arabic dialects and forms. In this paper, we present an in-depth review focused on studies published between 2019 and September 2023 related to Arabic offensive language and hate speech detection. To conclude, we highlighted the most significant methods, Arabic datasets, taxonomy analysis, and challenges. Moreover, this review provides a foundation of knowledge that can help the researchers design and implement reliable and more accurate solutions. [ABSTRACT FROM AUTHOR]

Emotions represent a fundamental aspect when evaluating user satisfaction or collecting customer feedback in human interactions, as well as in the realm of human–computer interface (HCI) technologies. Moreover, as human beings, we possess a distinctive capacity for communication through spoken language. Recently, the realm of Speech Emotion Recognition (SER) has garnered substantial interest and gained significant traction within the domain of Natural Language Processing (NLP). Its primary objective remains the identification of various emotions, such as sadness, neutrality, and anger, from audio speech using a diverse array of classifiers. This paper conducts a comprehensive critical analysis of the existing Arabic SER studies. Furthermore, this research delves into the performance and constraints associated with these previous works. It also sheds light on the current promising trends aimed at enhancing methods for recognizing emotions in speech. To the best of our knowledge, this research stands as a pioneering contribution to the SER field, particularly in the context of reviewing existing Arabic studies. [ABSTRACT FROM AUTHOR]

There is no doubt that the challenges facing Arabic language have become a concern that worries every Arab who is jealous of his language and identity, due to their severe effects on our children’s language, thought, and personality formation. Therefore, scholars of the Arab nation, university professors, school teachers, and those interested came together to confront these challenges. Conferences, seminars, and workshops were held to discuss them and propose solutions and remedies. Newspapers and magazines warned of their effects on the Arab nation, as they had become a threat to its reality and future. Despite this, these challenges still there, and we have not witnessed tangible steps to reduce the risk of decline in the use of the Standard Arabic language and its neglect at the local and Arab levels. This research addresses the challenges facing Arabic language in our Arab world with the aim of identifying their causes, effects and proposing ways to confront them. One of the most prominent of these challenges is globalization, as the globalization of the language emerged through the dominance of the English language and the promotion of its status at the expense of the Arabic language. The negative effects of Westernization have appeared in the identity and attitudes of our children, in addition to the widespread poor performance of students in Arabic language skills, which has become a phenomenon that calls for serious and careful attention because it has become a threat to the Arab nation in its identity, belief, and connection to its history and civilization. The research suggested ways to confront these challenges by activating the role of: the state, the university, the school, the family, and society, leading to suggesting some recommendations including: developing a strategy to enable the Arabic language to regain its sovereignty and effective role in civilizational construction, starting with issuing official decisions, legislation related to that, and developing awareness of the importance of the role Arabic language in strengthening the identity of the Arab nation and rooting its thought and culture, and ending with circulating standard Arabic in schools, universities, institutions and among members of society, by practicing the correct linguistic expression of our opinions and ideas in our scientific, practical and social life. [ABSTRACT FROM AUTHOR]

Detecting variations in dialects within a language can be challenging, particularly in regions with rich linguistic diversity like Saudi Arabia. To our knowledge, no prior attempts have been made to develop a multimodal, audio–textual framework for Saudi dialect detection. The current approaches often concentrate on detecting dialects only based on audio or textual data, which fails to capture the complex relationship between both modalities. In this paper, we propose a novel Multimodal Framework, called MF-Saudi, for Saudi dialect detection. The framework consists of three main components: (1) a pretrained BERT encoder for extracting and encoding textual information; (2) an acoustic model for representing audio signals and fusing them with textual information via the fusion layer; and (3) an alignment learning module to develop meaningful representations that capture the complexities of audio–text relationships, resulting in improved dialect detection. We conduct empirical evaluations on a real-world dataset, demonstrating that our solution outperforms some of the state-of-the-art baseline methods. The experiment’s code can be found here: https://github.com/raed19/MF-Saudi.

ArzEn-MultiGenre is a parallel dataset of Egyptian Arabic song lyrics, novels, and TV show subtitles that are manually translated and aligned with their English counterparts. The dataset contains 25,557 segment pairs that can be used to benchmark new machine translation models, fine-tune large language models in few-shot settings, and adapt commercial machine translation applications such as Google Translate. Additionally, the dataset is a valuable resource for research in various disciplines, including translation studies, cross-linguistic analysis, and lexical semantics. The dataset can also serve pedagogical purposes by training translation students and aid professional translators as a translation memory. The contributions are twofold: first, the dataset features textual genres not found in existing parallel Egyptian Arabic and English datasets, and second, it is a gold-standard dataset that has been translated and aligned by human experts.

Türkiye’nin güneydoğusunda bulunan Şanlıurfa’da Türkçenin yanı sıra Arapça, Kürtçe ve Zazaca da konuşulmaktadır. Şanlıurfa’da konuşulan Arapça, bedevi lehçeler grubunda tasnif edilen ve kendine has birtakım özellikler gösteren bir lehçedir. Türkçe ile uzun dönemlerden beridir doğrudan ilişkili olan ve baskın Türk dili karşısında zayıf dil konumuna düşen bu lehçe Türkçeden birçok sözcük ve ek ödünçleme yoluna gitmiştir. Şanlıurfa Arapçasında dikkat çeken hususlardan biri de Türkçe kelimelerin bazıları direkt değil, Arapça fiil ve isim formlarına aktarılarak alınmasıdır. Dolayısıyla alınan kelimeler mazi-muzari fiil formunda veya türetilmiş isim formunda karşımıza çıkmaktadır. Bu çalışma lehçenin Türkçeden ne derece etkilendiğini ve kelime ödünçlemede Arap dilinin kalıplarına ne derece sadık kaldığını ortaya koymaya çalışacaktır. Çalışmada saha çalışmasından elde edilmiş olan kişisel arşivden yararlanılmış, kelimeler ele alınıp hangi kalıba uyduğu incelenerek tasnif edilmiştir. Bu çalışma, yüzlerce yıldır Türkçe ile doğrudan etkileşim halinde olan bölgedeki Arap lehçesinin yapısını ortaya koymak açısından büyük önem arz etmektedir.

In recent years, models such as the transformer have demonstrated impressive capabilities in the realm of natural language processing. However, these models are known for their complexity and the substantial training they require. Furthermore, the self-attention mechanism within the transformer, designed to capture semantic relationships among words in sequences, faces challenges when dealing with short sequences. This limitation hinders its effectiveness in five-polarity Arabic sentiment analysis (SA) tasks. The switch-transformer model has surfaced as a potential substitute. Nevertheless, when employing one-task learning for their training, these models frequently face challenges in presenting exceptional performances and encounter issues when producing resilient latent feature representations, particularly in the context of small-size datasets. This challenge is particularly prominent in the case of the Arabic dialect, which is recognized as a low-resource language. In response to these constraints, this research introduces a novel method for the sentiment analysis of Arabic text. This approach leverages multi-task learning (MTL) in combination with the switch-transformer shared encoder to enhance model adaptability and refine sentence representations. By integrating a mixture of experts (MoE) technique that breaks down the problem into smaller, more manageable sub-problems, the model becomes skilled in managing extended sequences and intricate input–output relationships, thereby benefiting both five-point and three-polarity Arabic sentiment analysis tasks. The proposed model effectively identifies sentiment in Arabic dialect sentences. The empirical results underscore its exceptional performance, with accuracy rates reaching 84.02% for the HARD dataset, 67.89% for the BRAD dataset, and 83.91% for the LABR dataset, as demonstrated by the evaluations conducted on these datasets. [ABSTRACT FROM AUTHOR]

The article examines the current trends of youth speech in Qatar in the context of the language situation in the Arab countries of the Persian Gulf. The following phenomena were investigated: the code-switching between English and Arabic in the speech of Qatar University students; choice of language when native Qataris and people from other Arab countries communicate with their children; the transmission of Arabic text through a combination of Latin letters and numbers in online communication, which is known as Arabizi. The material of the research was the author's own observations during her studies at Qatar University (2018-2019) and her interviews with informants directly in Qatar (2022-2023), correspondence with speakers of the Qatari dialect via Facebook and WhatsApp, as well as materials from Internet resources. It was found that during conversations of young Qataris, about 40 % of the words used are from the English vocabulary, and 60 % - in the Qatari dialect, the vocabulary of which consists of words of Arabic origin which are common to the Modern Standard Arabic, words common for the Arabic dialects, as well as lexemes from sedentary or Bedouin dialects. At the same time, it was found that about 75 % of parents in autochthonous Qatari families speak to their children predominantly in Arabic, and 25 % - mainly in English. One of the reasons for this trend is the presence of nannies and housekeepers who communicate only in English. On the other hand, 60 % of young parents who are from other Arab countries and have been living in Qatar for a long time speak mainly English with their children. It was concluded that the trends in the speech of Qatar's youth are influenced by the studies at international schools and universities, and the presence of foreign nannies and housekeepers. [ABSTRACT FROM AUTHOR]

Parallel corpora for low-resource Arabic dialects and English are limited and small-scale, and most neural machine translation models, including Google Translate, rely mainly on parallel corpora of standard Arabic and English to train for dialectal Arabic translation. A model well trained to translate to and from standard Arabic is believed to efficiently translate dialectal Arabic, given their similarities. This study demonstrates the impact of not using large-scale, dialect-specific parallel corpora by quantitatively and qualitatively analyzing the performance of Google Translate in translating Egyptian Arabic adjuncts. Compared to human reference translation, Google Translate achieved a low BLEU score of 14.69. Qualitative analysis showed that reliance on standard Arabic parallel corpora caused a negative transfer problem manifested in the literal translation of idiomatic adjuncts, the misinterpretation of dialectal adjuncts as main clause constituents, the translation of dialectal adjuncts after orthographically similar standard Arabic words, and the use of standard Arabic common lexical meanings to translate dialect-specific adjuncts. This study’s findings will be relevant for researchers interested in dialectal Arabic neural machine translation and has implications for investment in the development of large-scale, dialect-specific corpora to better process the peculiarities of Arabic dialects and reduce the effect of negative transfer from standard Arabic. [ABSTRACT FROM AUTHOR]

In the realm of the five-category classification endeavor, there has been limited exploration of applied techniques for classifying Arabic text. These methods have primarily leaned on single-task learning, incorporating manually crafted features that lack robust sentence representations. Recently, the Transformer paradigm has emerged as a highly promising alternative. However, when these models are trained using single-task learning, they often face challenges in achieving outstanding performance and generating robust latent feature representations, especially when dealing with small datasets. This issue is particularly pronounced in the context of the Arabic dialect, which has a scarcity of available resources. Given these constraints, this study introduces an innovative approach to dissecting sentiment in Arabic text. This approach combines Inductive Transfer (INT) with the Transformer paradigm to augment the adaptability of the model and refine the representation of sentences. By employing self-attention SE-A and feed-forward sub-layers as a shared Transformer encoder for both the five-category and three-category Arabic text classification tasks, this proposed model adeptly discerns sentiment in Arabic dialect sentences. The empirical findings underscore the commendable performance of the proposed model, as demonstrated in assessments of the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset. [ABSTRACT FROM AUTHOR]

Automatic Speech Recognition (ASR) has standard rules which must be followed and considered carefully. Some difficulties that lead to less ASR performance is variations in pronunciation and small words misrecognition. Arabic ASR faces some challenges like difficulty in obtaining corpora for spoken dialects. Obtaining a wide range of diacritized text as well as the enormous number of word forms is considered a major challenge due to the Arabic language morphology richness and its’ letters capability to be written without diacritics. Although Arabic is one of the most popular languages, Arabic ASR systems are still rare compared with other languages. As ASR systems depend primarily on speech corpuses, Arabic ASR systems requires specific-dialect speech corpuses. Such speech corpuses are still deficient, costly, nor sometimes exists. In this research, we contribute to overcome the lack of speech recognition and misunderstanding for one of the most famous dialects in Saudi Arabia, Medina. We created a brand-new corpus “Haneen Corpus“, which consists of 70,364 tokens that have been uttered using Medina dialect, and constructed a dictionary using 64 phonemes to analyse the correct pronunciation of words. Our Medina Dialect ASR System exploited Hidden Markov Models (HMM) that achieved 92.09 % speech recognition accuracy.

[rev. of] S. BETTEGA, L. D’ANNA, Gender and Number Agreement in Arabic, Brill, 2023

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Sentiment analysis is the process of examining people's opinions and emotions towards goods, services, organizations, individuals, and other things, through the use of textual data. It involves categorizing text as positive, negative, or neutral to quantify people's beliefs. Social media platforms have become an important source of sentiment analysis data due to their widespread use for sharing opinions and information. As the number of social media users continues to grow, the amount of data generated for sentiment analysis also increases. Previous research on sentiment analysis for the Arabic language has mostly focused on Modern Standard Arabic and various dialects such as Egyptian, Saudi, Algerian, Jordanian, Tunisian, and Levantine. However, there has been no research on the utilization of deep-learning approaches for sentiment analysis of the Emirati dialect, which is an informal form of the Arabic language spoken in the United Arab Emirates. It's important to note that each country in the Arab world has its dialect, and some dialects may even have several sub-dialects.The primary aim of this research is to create a highly advanced deep-learning model that can effectively perform sentiment analysis on the Emirati dialect. To achieve this objective, the authors have proposed and utilized seven different deep-learning models for sentiment analysis of the Emirati dialect. Then, an ensemble stacking model was introduced to combine the best-performing deep learning models used in this study. The ensemble stacking deep learning model consisted of deep learning models with a meta-learner layer of classifiers. The first model combined the two best-performing deep learning models, the second combined the four best-performing models, and the final model combined all seven trained deep learning models in this research. The proposed ensemble stacking deep learning model was assessed on four datasets, three versions of the ESAAD Emirati Sentiment Analysis Annotated Dataset, two versions of Twitter-based Benchmark Arabic Sentiment Analysis Dataset (ASAD), an Arabic Company Reviews dataset, and an English dataset known as a Preprocessed Sentiment Analysis Dataset PSAD. The results of the experiments demonstrated that the proposed ensemble stacking model presented an outstanding performance in terms of accuracy and achieved an accuracy of 95.54% for the ESAAD dataset, 96.71% for the ASAD benchmark dataset, 96.65% for the Arabic Company Reviews Dataset, and 98.53% for the Preprocessed Sentiment Analysis Dataset PSAD.

This article examines the potential of pedagogical translanguaging in a community/heritage language context. With focus on Arabic as a multidialectal and multiglossic language, the paper primarily examines the function of translanguaging practices in teacher-learner and learner-learner interaction to construct and negotiate linguistic knowledge in the standard variety of the language. The results show that the learners' linguistic repertoires (multiple varieties of Arabic and English) are actively and dynamically employed in the exchanges to negotiate linguistic knowledge (lexical and grammatical) in a setting that venerates the standard variety as a medium of instruction with a monolingual policy. Also, the results show how these multidialectal practices are sometimes utilised to acknowledge and give voice to the heritage learners' dialectal identities. It is argued that community/heritage language learning contexts are ideal translanguaging spaces in which heritage language learners find ample opportunities for identity negotiation and knowledge construction. These opportunities are augmented in a classroom atmosphere that gives legitimacy to their dialects and challenges the monolingual ideology. Pedagogical implications are discussed for Arabic heritage and mainstream second language programmes with heritage learners. [ABSTRACT FROM AUTHOR]

Language is a living phenomenon and consists of many components, including phonemes, part phonemes, which are vowel and consonant phonemes, syllables, supravocal stress, and morphemes which are the ways of inflection and derivation of words. A language, which develops and changes in terms of sound, composition, syntax, consumption, and meaning in time and space, shows a difference between the age in which it was spoken and the previous age. Since Arabic is the language of the Qur'an, the language used in Quranic Arabic (classical Arabic) is protected within the framework of grammatical rules. However, the local (regional) elements used in daily life in the dialect dimension called Ammiyyah (Colloquial Arabic) differ from both Quranic Arabic and the dialects that vary based on geography. In this study, distinctions are made between the concepts of language, dialect, and accent. Then, sound changes, consumable changes, and local word usages in the Modern Jordanian dialect, which is a dialect of Arabic, are emphasized. What is more, it is aimed at explaining with examples how the sound changes that occur in the Jordanian dialect differ from the Quranic Arabic are between vowels and consonants. In addition, the explain of words and compositions with regard to local usage areas by focusing on the special usage areas of the Jordanian dialect, which differs from morphological prosody in the Arabic of the Qur'an, is done by using document analysis, one of the qualitative research methods. [ABSTRACT FROM AUTHOR]

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Copyright of Journal Dawat is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose – This study aims to identify and measure the lexical gap between the old and young generations in the Jizani dialect and determine the causes of that gap. Design/methodology/approach – A 20-item questionnaire was distributed randomly among 104 participants. Next, 12 participants were selected and interviewed. SPSS software was used to analyse the quantitative data from the questionnaire. The data elicited from the interviews was qualitatively analysed, considering age and gender factors. Findings – The major findings revealed that a lexical gap between old and young language speakers in the Jizani dialect exists. The gap between young females and the older generation was greater than that between young and old males. Some old words are likely to disappear in the coming decades. Social media, which is a time-consuming and word-borrowing medium for young people, was one of the reasons, besides the tendency of females to use prestigious words. Originality/value – This study attempted to find the differences between the vocabularies of old and young speakers. If it does exist, is it significant? What are the reasons for this lexical gap? This will help other researchers and dialectologists register the old words before they die out and try to bridge that lexical gap.

Background: Speech is simply an acoustic signal that carries linguistic information. Sound is the basic requirement for speech production, initiated by a simple disturbance of air molecules in the vocal passage is provided by movement of certain body organs such as chest muscles, vocal cords, lips, teeth, tongue, palates etc. finally producing spoken language. Speech signal is a multidimensional acoustic wave, which provides information about the words or message being spoken expressing speaker ideas, feelings and emotions to the listener reflecting speaker identity, physical and mental health, race, age, sex, education level, religious orientation and overall individual background. The Variation in speech production might be explained by several factors including inter-speakers and intra-speaker variations. The inter-speakers variation includes idiosyncratic and social-indexical differences as well as differences in vocal tract size as a result of age and sex differences. Therefore, two different speakers may articulate the same intended sound with different acoustic patterns and different sounds with the same acoustic patterns. Speech sounds may be classified under two major categories: vowels and consonants. The 28 consonants in MSA, 3 short vowels (/a/,/u/,/i/), 3 longer vowels (/a:/,/u:/,/i:/), and two diphthongs(/ai/,/au/). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Anbar University for Languages & Literature / Magallat Gami'at Al-Anbar Li-Lugat Wa-al-Adabl is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, models such as the transformer have demonstrated impressive capabilities in the realm of natural language processing. However, these models are known for their complexity and the substantial training they require. Furthermore, the self-attention mechanism within the transformer, designed to capture semantic relationships among words in sequences, faces challenges when dealing with short sequences. This limitation hinders its effectiveness in five-polarity Arabic sentiment analysis (SA) tasks. The switch-transformer model has surfaced as a potential substitute. Nevertheless, when employing one-task learning for their training, these models frequently face challenges in presenting exceptional performances and encounter issues when producing resilient latent feature representations, particularly in the context of small-size datasets. This challenge is particularly prominent in the case of the Arabic dialect, which is recognized as a low-resource language. In response to these constraints, this research introduces a novel method for the sentiment analysis of Arabic text. This approach leverages multi-task learning (MTL) in combination with the switch-transformer shared encoder to enhance model adaptability and refine sentence representations. By integrating a mixture of experts (MoE) technique that breaks down the problem into smaller, more manageable sub-problems, the model becomes skilled in managing extended sequences and intricate input–output relationships, thereby benefiting both five-point and three-polarity Arabic sentiment analysis tasks. The proposed model effectively identifies sentiment in Arabic dialect sentences. The empirical results underscore its exceptional performance, with accuracy rates reaching 84.02% for the HARD dataset, 67.89% for the BRAD dataset, and 83.91% for the LABR dataset, as demonstrated by the evaluations conducted on these datasets.

In the realm of the five-category classification endeavor, there has been limited exploration of applied techniques for classifying Arabic text. These methods have primarily leaned on single-task learning, incorporating manually crafted features that lack robust sentence representations. Recently, the Transformer paradigm has emerged as a highly promising alternative. However, when these models are trained using single-task learning, they often face challenges in achieving outstanding performance and generating robust latent feature representations, especially when dealing with small datasets. This issue is particularly pronounced in the context of the Arabic dialect, which has a scarcity of available resources. Given these constraints, this study introduces an innovative approach to dissecting sentiment in Arabic text. This approach combines Inductive Transfer (INT) with the Transformer paradigm to augment the adaptability of the model and refine the representation of sentences. By employing self-attention SE-A and feed-forward sub-layers as a shared Transformer encoder for both the five-category and three-category Arabic text classification tasks, this proposed model adeptly discerns sentiment in Arabic dialect sentences. The empirical findings underscore the commendable performance of the proposed model, as demonstrated in assessments of the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

Any natural language may have dozens of accents. Even though the equivalent phonemic formation of the word, if it is properly called in different accents, humans do have audio signals that are distinct from one another. Among the most common issues with speech, the processing is discrepancies in pronunciation, accent, and enunciation. This research study examines the issues of detecting, fixing, and summarising accent defects of average Arabic individuals in English-speaking speech. The article then discusses the key approaches and structure that will be utilized to address both accent flaws and pronunciation issues. The proposed SpeakCorrect computerized interface employs a cuttingedge speech recognition system and analyses pronunciation errors with a speech decoder. As a result, some of the most essential types of changes in pronunciation that are significant for speech recognition are performed, and accent defects defining such differences are presented. Consequently, the suggested technique increases the Speaker's accuracy. SpeakCorrect uses 100 h of phonetically prepared individuals to construct a pronunciation instruction repository. These prerecorded sets are used to train Hidden Markov Models (HMM) as well as weighted graph systems. Their speeches are quite clear and might be considered natural. The proposed interface is optimized for use with an integrated phonetic pronounced dataset, as well as for analyzing and identifying speech faults in Saudi and Egyptian dialects. The proposed interface detects, analyses, and assists English learners in correcting utterance faults, overcoming problems, and improving their pronunciations. [ABSTRACT FROM AUTHOR]

This study introduces and analyzes proximal and distal singular demonstratives in fourteen varieties of Musandam Arabic, a little-documented dialect group located on Musandam Peninsula in northern Oman and neighbouring areas of the United Arab Emirates. Following an overview of the dialect group in its regional context, the study provides a description of singular demonstratives from the point of view of phono-logy, morphology, and geographical distribution. The study then focuses on two salient features found in several of the varieties under investigation: gender distinction based on consonantal alternation (d -based masculine forms vs t -based feminine forms); and gemination of the feminine t -element. While the former is attested, albeit rarely, in other Arabic dialects, the latter is unheard of. In the last section of the article, some hypotheses are put forward as to how these forms could have developed from a historical point of view, in light of data from different Arabic and Semitic varieties. While the gemination of the t -element is best regarded as a Musandam-internal innovation, the d : t consonantal alternation reinforces the putative historical link between south-western Arabia and Oman. [ABSTRACT FROM AUTHOR]

In the existential domain, Classical Arabic expresses the ground > figure perspectivization in locational predication by a mere change in constituent order, but Modern Arabic varieties have variously grammaticalized existential particles that tend to acquire verb-like properties. In the possessive domain, Classical Arabic and Modern Standard Arabic have a typical oblique-possessor (or locational possessive) construction in which the possessor phrase is flagged by a preposition. In the vernacular varieties, this preposition has become a possessive predicator with some verbal properties, whose coding frame is similar (although not fully identical) to that of a transitive verb. More radical changes in the existential and possessive domains are attested in pidginized/creolized Arabic varieties. [ABSTRACT FROM AUTHOR]

Arabic is a typical noun-attribute language with head-driven agreement in gender and number. As for definite attributive NPs, the default in most spoken Arabic varieties is definite marking of both the noun and the adjective – a feature that is rarely found cross-linguistically. This article shows that there are also cases of reversed word order, lacking agreement in gender, and dialects with definite marking on the nominal head. In dialects at the northern fringe of the Fertile Crescent, definiteness is by default only marked on the adjective. In these dialects, adjectival attributes are structurally identical to nominal attributes, including the use of the construct state with feminine heads. [ABSTRACT FROM AUTHOR]

Pain Constructions (PCs) constitute a class of experiential constructions expressing situations that involve unpleasant physical experiences (e.g. headache, burning eyes, dizziness, etc.). Previous cross-linguistic comparisons have shown that, though languages do not have dedicated morphosyntactic structures for encoding pain, there are certain constructions that are more likely to express physical experiences. Based on original data elicited by means of a situational questionnaire, this paper aims at analyzing the semantic and syntactic properties of PCs in modern Arabic dialects and to make typological generalizations about their cross-dialect variation. Benefiting from insights from both linguistic typology and contact linguistics, the study eventually shows that, despite considerable lexicosemantic and morphosyntactic variation, PCs in Arabic can be reduced to two main syntactic types: locational and inverse constructions. [ABSTRACT FROM AUTHOR]

This paper describes and compares the strategies used in Arabic dialects to encode the progressive aspect, in order to show their similarities and what sets them apart from one another, as well as to situate them within cross-linguistic tendencies. Drawing on a wide variety of data, the paper shows the different ways the progressive aspect was (or is being) grammaticalized in Arabic dialects in light of the typologically common paths of grammaticalization. These paths involve for some dialects the reorganization of their aspectual categories, and thus provide an interesting perspective to look at the evolution of aspectual systems in Arabic varieties. [ABSTRACT FROM AUTHOR]

Languages with a grammatical structure that have a free order for words, such as Arabic dialects, are considered a challenge for neural machine translation (NMT) models because of the attached suffixes, affixes, and out-of-vocabulary words. This paper presents a new reverse positional encoding mechanism for a multi-head attention (MHA) neural machine translation (MT) model to translate from right-to-left texts such as Arabic dialects (ADs) to modern standard Arabic (MSA). The proposed model depends on an MHA mechanism that has been suggested recently. The utilization of the new reverse positional encoding (RPE) mechanism and the use of sub-word units as an input to the self-attention layer improve this sublayer for the proposed model's encoder by capturing all dependencies between the words in right-to-left texts, such as AD input sentences. Experiments were conducted on Maghrebi Arabic to MSA, Levantine Arabic to MSA, Nile Basin Arabic to MSA, Gulf Arabic to MSA, and Iraqi Arabic to MSA. Experimental analysis proved that the proposed reverse positional encoding MHA NMT model was efficiently able to handle the open grammatical structure issue of Arabic dialect sentences, and the proposed RPE MHA NMT model enhanced the translation quality for right-to-left texts such as Arabic dialects. [ABSTRACT FROM AUTHOR]