Arabic Machine Translation: A Survey With Challenges and Future Directions

Zakraoui, Jezia; Saleh, Moutaz; Al-Maadeed, Somaya; Alja’am, Jihad Mohamad

doi:10.1109/access.2021.3132488

Cited by 17 publications

(9 citation statements)

References 84 publications

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“…Wasfi At-Tall was a prominent political figure and statesman in Jordan, serving as the Prime Minister on multiple occasions during the 1960s and early 1970s (Susser, 1994;Joyce, 2008;Morris, 2001). Known for his charismatic leadership and powerful oratory skills, At-Tall was revered for his ability to connect with the masses through his speeches.…”

Section: Prime Minister Wasfi At-tall's Speechesmentioning

confidence: 99%

“…This unique blend allowed him to effectively communicate and resonate with a diverse audience, transcending social and educational barriers. At-Tall's speeches were characterized by his passionate delivery and persuasive rhetoric, making them memorable and influential (Susser, 1994). His choice of language, including colloquial Arabic expressions, added a touch of authenticity and relatability, enabling him to connect with the people of the time.…”

Section: Prime Minister Wasfi At-tall's Speechesmentioning

confidence: 99%

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Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms

Aldawsari

2024

Preprint

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This study examines the advancements in AI-driven machine translation, specifically focusing on the accurate translation of Arabic colloquial expressions. It aims to assess the progress made by Large Language Models, such as Bing AI Chat, compared to traditional machine translation systems. By focusing on colloquial expressions, this research aims to shed light on the challenges and opportunities for improvement in machine translation systems, particularly when dealing with the complexities of translating informal Arabic utterances. Building upon At-tall’s 2019 thesis, which compared Google Translate and human translators, the study employs the same Arabic sentences as a test dataset, allowing for a direct comparison between 2019 translations and those produced by current machine translation tools. The findings indicate limited improvement in Google Translate since 2019, with Bing Translator exhibiting a similar level of translation accuracy. In contrast, Bing AI Chat consistently outperformed the other systems, showcasing the potential of Large Language Model machine translation. Notably, Bing AI Chat provided interpretations and valuable comments on the tested Arabic phrases, demonstrating a deeper understanding of the intended meaning. This study contributes significantly to the field of machine translation by providing evidence of the potential of Large Language Model systems in producing more accurate Arabic-English translations. It emphasizes the advantage of Large Language Models in dealing with non-standard Arabic expressions, encouraging further exploration of Large Language Model-powered approaches in machine translation. The findings offer a promising pathway towards achieving more accurate and expressive translations across diverse languages and cultures.

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Section: Prime Minister Wasfi At-tall's Speechesmentioning

confidence: 99%

Section: Prime Minister Wasfi At-tall's Speechesmentioning

confidence: 99%

Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms

Aldawsari

2024

Preprint

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“…Depending on the context in which they appear in a word, Arabic letters may be written in a wide variety of different ways. For instance, the letter "‫/هأ‬ha" may appear like ‫هـ"‬ ‫ـهـ/‬ ‫"ـه/‬ depending on where it is in the word, whether it is at the beginning, in the middle, or at the conclusion of the sentence [13,14]. Take, for example:…”

Section: Various Forms Of Lettersmentioning

confidence: 99%

“…For instance, the vast majority of Arabic material seen on social media platforms is composed in an informal vernacular. This indicates that it makes use of local terminology as well as slang words that vary depending on the region of the Arabic-speaking world in which one is located [13].…”

Section: Arabic Dialects Varymentioning

confidence: 99%

Python Solutions to Address Natural Language Challenges

Moaiad,

Alobed,

Alsakhnini

2024

ijmst

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Arabic is one of six official languages, according to UNESCO. It's spoken by more than 422 million Arabs, and 1.5 billion Muslims around the world use it when they pray five times a day. Arabs spoke classical Arabic more than 1400 years ago. On the other hand, dialectal Arabic is the everyday language that is used informally and varies from region to region. Modern Standard Arabic borrows from and adds to other languages to fit the needs of its speakers. Arabic is harder to learn because there are three different ways to speak it: the classical way, the modern way, and the casual way. Arabic is hard to work with on computers for more than one reason. Because Arabic has a lot of inflection and derivation, one lemma can turn into many different words with different meanings.

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“…It is noteworthy to know that it is one of the six official languages of the United Nations. Some ANLP work in literature includes sentiment analysis [1], machine translation [2], dialect identification [3], Named Entity Recognition NER [4], question answering [5], summarization [6], etc. What makes Arabic special and challenging at the same time is the diacritization.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep-learning based Approach for Automatic Diacritization of Arabic Poems using Sequence-to-Sequence Model

Mahmoud¹,

Negied²

2023

IJACSA

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Over the last 10 years, Arabic language have attracted researchers in the area of Natural Language Processing (NLP). A lot of research papers suddenly emerged in which the main work was the processing of Arabic language and its dialects too. Arabic language processing has been given a special name ANLP (Arabic Natural Language Processing). A lot of ANLP work can be found in literature including almost all NLP applications. Many researchers have been attracted also to Arabic linguistic knowledge. The work expands from Basic Language Analysis to Semantic Level Analysis. But Arabic text semantic analysis cannot be held without considering diacritization, which can greatly affect the meaning. Many Arabic texts are written without diacritization, and Diacritizing them manually is a very tiresome process that may need an expert. Automatic diacritization systems became a demand as an initial step for processing Arabic text for any Arabic Language Processing application as Arabic diacritization is very important to get a readable and understandable Arabic text. For this reason, many researchers recently worked on building systems and tools that automatically diacritize un-diacritized Arabic texts. This work presents a novel deep learning-based sequenceto-sequence model to diacritize un-diacritized Arabic poems. The proposed model was tested and achieved high diacritization accuracy rate.

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Arabic Machine Translation: A Survey With Challenges and Future Directions

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 17 publications

References 84 publications

Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms

Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms

Python Solutions to Address Natural Language Challenges

A Novel Deep-learning based Approach for Automatic Diacritization of Arabic Poems using Sequence-to-Sequence Model

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