A Hybrid Rules and Statistical Method for Arabic to English Machine Translation

Alqudsi, Arwa; Omar, Nazlia; Shaker, Khalid

doi:10.1109/cais.2019.8769545

Cited by 10 publications

(13 citation statements)

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“…The Arabic morphology and syntax in addition to other linguistic aspects has made the automatic translation from and to Arabic more challenging. Therefore, it is possible for the size of vocabularies to reach a huge number [7] causing a main computational constraint. Although, there has been a significant improvement due to the recent advances in MT approaches such as statistical, neural methods and hybrid models, these linguistic aspects are still causing many difficulties.…”

Section: Survey Frameworkmentioning

confidence: 99%

“…Therefore, the demand for quick and accurate machine translations is growing accordingly. However, finding appropriate and optimal translation is not an easy task in any language setting [6], [7], [8]. Several machine translation systems already exist, such as Sakhr 1 , Al-Mutarjim™ Al-Arabey 3.0 2 and SYSTRAN 3 , but the quality of the translation with regards to the Arabic language needs to be further improved due to many issues reported in various research works such as linguistic errors [6], [9], [10], [11], 1 http://www.sakhr.com/index.php/en/ 2 https://al-mutarjim-al-arabey.software.informer.com/3.0/ 3 https://www.systransoft.com/ 4 https://translation.babylon-software.com/ [12], [13].…”

Section: Introductionmentioning

confidence: 99%

“…Several machine translation systems already exist, such as Sakhr 1 , Al-Mutarjim™ Al-Arabey 3.0 2 and SYSTRAN 3 , but the quality of the translation with regards to the Arabic language needs to be further improved due to many issues reported in various research works such as linguistic errors [6], [9], [10], [11], 1 http://www.sakhr.com/index.php/en/ 2 https://al-mutarjim-al-arabey.software.informer.com/3.0/ 3 https://www.systransoft.com/ 4 https://translation.babylon-software.com/ [12], [13]. In addition, there are web-based MT systems such as Babylon 4 , Bing Translator 5 , Google Translate 6 , and Shaheen 7 that can translate a source text from Arabic to English and vice-versa.…”

Section: Introductionmentioning

confidence: 99%

“…pose significant linguistic challenges to MT [14]. For instance, preprocessing the Arabic source by morphological segmentation [15], [16], syntactical reordering [17], hybridization [7], [18] etc. has been shown to improve the performance of AMT [7], [15], [16], [17], [19], [18].…”

Section: Introductionmentioning

confidence: 99%

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Arabic Machine Translation: A Survey With Challenges and Future Directions

et al. 2021

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Section: Survey Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Arabic Machine Translation: A Survey With Challenges and Future Directions

et al. 2021

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“…In a more recent study, Alqudsi et al (2019) proposed a hybrid strategy that integrates the rulebased approach with a statistical algorithm in order to address the problems of word ordering and ambiguity. Their results reaffirm that the improvement of Arabic<>English translation is conditional on the size and relevance of the available training data.…”

Section: Arabic-english Translation Research Pre-neural Networkmentioning

confidence: 99%

Comparative Evaluation of Translation Memory (TM) and Machine Translation (MT) Systems in Translation between Arabic and English

Khaled¹

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In general, advances in translation technology tools have enhanced translation quality significantly. Unfortunately, however, it seems that this is not the case for all language pairs. A concern arises when the users of translation tools want to work between different language families such as Arabic and English. The main problems facing Arabic<>English translation tools lie in Arabic’s characteristic free word order, richness of word inflection – including orthographic ambiguity – and optionality of diacritics, in addition to a lack of data resources. The aim of this study is to compare the performance of translation memory (TM) and machine translation (MT) systems in translating between Arabic and English.The research evaluates the two systems based on specific criteria relating to needs and expected results. The first part of the thesis evaluates the performance of a set of well-known TM systems when retrieving a segment of text that includes an Arabic linguistic feature. As it is widely known that TM matching metrics are based solely on the use of edit distance string measurements, it was expected that the aforementioned issues would lead to a low match percentage. The second part of the thesis evaluates multiple MT systems that use the mainstream neural machine translation (NMT) approach to translation quality. Due to a lack of training data resources and its rich morphology, it was anticipated that Arabic features would reduce the translation quality of this corpus-based approach. The systems’ output was evaluated using both automatic evaluation metrics including BLEU and hLEPOR, and TAUS human quality ranking criteria for adequacy and fluency.The study employed a black-box testing methodology to experimentally examine the TM systems through a test suite instrument and also to translate Arabic English sentences to collect the MT systems’ output. A translation threshold was used to evaluate the fuzzy matches of TM systems, while an online survey was used to collect participants’ responses to the quality of MT system’s output. The experiments’ input of both systems was extracted from Arabic<>English corpora, which was examined by means of quantitative data analysis. The results show that, when retrieving translations, the current TM matching metrics are unable to recognise Arabic features and score them appropriately. In terms of automatic translation, MT produced good results for adequacy, especially when translating from Arabic to English, but the systems’ output appeared to need post-editing for fluency. Moreover, when retrievingfrom Arabic, it was found that short sentences were handled much better by MT than by TM. The findings may be given as recommendations to software developers.

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Digital Hadith authentication: Recent advances, open challenges, and future directions

Hakak

Kamsin

Khan

et al. 2020

Trans Emerging Tel Tech

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The Holy Quran and Hadith are the two main sources of legislation and guidelines for Muslims to shape their lives. The daily activities, sayings, and deeds of the Holy Prophet Muhammad (PBUH) are called Hadiths. Hadiths are the optimal practical descriptions of the Holy Quran. Technological advancements of information and communication technologies (ICT) have revolutionized every field of daily life, including digitizing the Holy Quran and Hadith. Available online contents of Hadith are obtained from different sources. Thus, alterations and fabrications of fake Hadiths are feasible. Authentication of these online available Hadith contents is a complex and challenging task and a crucial area of study in Islam. Few Hadith authentication techniques and systems are proposed in the literature. In this study, we have surveyed all techniques and systems, which are proposed for Hadith authentication. Furthermore, classification, open challenges, and future research directions related to Hadith authentication are identified.

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A Hybrid Rules and Statistical Method for Arabic to English Machine Translation

Cited by 10 publications

References 13 publications

Arabic Machine Translation: A Survey With Challenges and Future Directions

Arabic Machine Translation: A Survey With Challenges and Future Directions

Comparative Evaluation of Translation Memory (TM) and Machine Translation (MT) Systems in Translation between Arabic and English

Digital Hadith authentication: Recent advances, open challenges, and future directions

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