scholarly journals Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation

2021 ◽  
Author(s):  
El Moatez Billah Nagoudi ◽  
AbdelRahim Elmadany ◽  
Muhammad Abdul-Mageed
Keyword(s):  
Machine Translation ◽  
Modern Standard Arabic ◽  
Standard Arabic ◽  
Modern Standard

A survey of automatic Arabic diacritization techniques

2013 ◽  
Vol 21 (3) ◽  
pp. 477-495 ◽  
Author(s):  
AQIL M. AZMI ◽  
REHAM S. ALMAJED
Keyword(s):  
Information Retrieval ◽  
Machine Translation ◽  
Arabic Language ◽  
Future Trend ◽  
Text To Speech ◽  
Modern Standard Arabic ◽  
Standard Arabic ◽  
The Future ◽  
Modern Standard

AbstractIn Modern Standard Arabic texts are typically written without diacritical markings. The diacritics are important to clarify the sense and meaning of words. Lack of these markings may lead to ambiguity even for the natives. Often the natives successfully disambiguate the meaning through the context; however, many Arabic applications, such as machine translation, text-to-speech, and information retrieval, are vulnerable due to lack of diacritics. The process of automatically restoring diacritical marks is called diacritization or diacritic restoration. In this paper we discuss the properties of the Arabic language and the issues that are related to the lack of the diacritical marking. It will be followed by a survey of the recent algorithms that were developed to solve the diacritization problem. We also look into the future trend for researchers working in this area.

scholarly journals A Transformer-Based Neural Machine Translation Model for Arabic Dialects That Utilizes Subword Units

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6509
Author(s):  
Laith H. Baniata ◽  
Isaac. K. E. Ampomah ◽  
Seyoung Park
Keyword(s):  
Machine Translation ◽  
Target Language ◽  
Modern Standard Arabic ◽  
Neural Machine Translation ◽  
Unknown Word ◽  
Translation Model ◽  
Standard Arabic ◽  
Arabic Dialects ◽  
Transformer Model ◽  
Modern Standard

Languages that allow free word order, such as Arabic dialects, are of significant difficulty for neural machine translation (NMT) because of many scarce words and the inefficiency of NMT systems to translate these words. Unknown Word (UNK) tokens represent the out-of-vocabulary words for the reason that NMT systems run with vocabulary that has fixed size. Scarce words are encoded completely as sequences of subword pieces employing the Word-Piece Model. This research paper introduces the first Transformer-based neural machine translation model for Arabic vernaculars that employs subword units. The proposed solution is based on the Transformer model that has been presented lately. The use of subword units and shared vocabulary within the Arabic dialect (the source language) and modern standard Arabic (the target language) enhances the behavior of the multi-head attention sublayers for the encoder by obtaining the overall dependencies between words of input sentence for Arabic vernacular. Experiments are carried out from Levantine Arabic vernacular (LEV) to modern standard Arabic (MSA) and Maghrebi Arabic vernacular (MAG) to MSA, Gulf–MSA, Nile–MSA, Iraqi Arabic (IRQ) to MSA translation tasks. Extensive experiments confirm that the suggested model adequately addresses the unknown word issue and boosts the quality of translation from Arabic vernaculars to Modern standard Arabic (MSA).

scholarly journals A Multitask-Based Neural Machine Translation Model with Part-of-Speech Tags Integration for Arabic Dialects

2018 ◽  
Vol 8 (12) ◽  
pp. 2502 ◽  
Author(s):  
Laith H. Baniata ◽  
Seyoung Park ◽  
Seong-Bae Park
Keyword(s):  
Machine Translation ◽  
Modern Standard Arabic ◽  
Neural Machine Translation ◽  
Standard Arabic ◽  
Translation Quality ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Arabic Dialects ◽  
Pos Tagger ◽  
Modern Standard

The statistical machine translation for the Arabic language integrates external linguistic resources such as part-of-speech tags. The current research presents a Bidirectional Long Short-Term Memory (Bi-LSTM) - Conditional Random Fields (CRF) segment-level Arabic Dialect POS tagger model, which will be integrated into the Multitask Neural Machine Translation (NMT) model. The proposed solution for NMT is based on the recurrent neural network encoder-decoder NMT model that has been introduced recently. The study has proposed and developed a unified Multitask NMT model that shares an encoder between the two tasks; Arabic Dialect (AD) to Modern Standard Arabic (MSA) translation task and the segment-level POS tagging tasks. A shared layer and an invariant layer are shared between the translation tasks. By training translation tasks and POS tagging task alternately, the proposed model can leverage the characteristic information and improve the translation quality from Arabic dialects to Modern Standard Arabic. The experiments are conducted from Levantine Arabic (LA) to MSA and Maghrebi Arabic (MA) to MSA translation tasks. As an additional linguistic resource, the segment-level part-of-speech tags for Arabic dialects were also exploited. Experiments suggest that translation quality and the performance of POS tagger were improved with the implementation of multitask learning approach.

The effects of motivation on processing instruction in the acquisition of Modern Standard Arabic gender agreement

2018 ◽  
Vol 2 (1) ◽  
pp. 61-82
Author(s):  
Ayah Farhat ◽  
Alessandro Benati
Keyword(s):  
Academic Motivation ◽  
Processing Instruction ◽  
Gender Agreement ◽  
Modern Standard Arabic ◽  
Language Background ◽  
Standard Arabic ◽  
Positive Effects ◽  
Sentence Level ◽  
Post Test ◽  
Modern Standard

The present study investigates the effects of motivation and processing instruction on the acquisition of Modern Standard Arabic gender agreement. The role of individual differences (e.g. age, gender, aptitude, language background and working memory) on the positive effects generated by processing instruction has been investigated in the last few years. However, no previous research has been conducted to measure the possible effects of motivation on L2 learners exposed to processing instruction. In addition, a reasonable question to be addressed within the processing instruction research framework is whether its positive effects can be generalised to the acquisition of Modern Standard Arabic. The Academic Motivation Scale (AMS) and the Attitude Motivation Test Battery (AMTB) motivation questionnaires were used to capture different variables that influence motivation in order to create the two different groups (high and low motivated). In this experimental study, forty-one native English school-age learners (aged 8–11) were assigned to two groups: ‘the high motivated group’ (n = 29): and the ‘low motivated group’ (n = 12). Both groups received processing instruction, which lasted for three hours. Sentence-level interpretation and production tasks were used in a pre-test and post-test design to measure instructional effects. The learners were required to fill in gaps in both written and spoken mode for the activities. The study also included a delayed post-test administered to the two groups four weeks later. The results indicated that both groups improved equally from pre-test to post-test in all assessment measures and they both retained the positive effects of the training in the delayed posttests. Processing instruction was proved to be the main factor for the improvement in performance regardless of the learner’s level of motivation.

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