Unsupervised dialectal neural machine translation

Farhan, Wael; Talafha, Bashar; Abuammar, Analle; Jaikat, Ruba; Al‐Ayyoub, Mahmoud; Tarakji, Ahmad Bisher; Toma, Anas

doi:10.1016/j.ipm.2019.102181

Cited by 31 publications

(25 citation statements)

References 9 publications

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“…Our results correspond to the findings of [1] which show that it is possible to obtain 70-80% accuracy in machine translation using artificial intelligence (multilingual NMT models); however, the rest is still a task for human translation.…”

Section: Discussionsupporting

confidence: 89%

“…Machine translation (MT) is a sub-field of computational linguistics that primarily focuses on automatic translation from one natural language into another natural language without any intervention [1].…”

Section: Introductionmentioning

confidence: 99%

“…NMT works on the principle of predicting the probability of a word sequence. Using sequence-to-sequence models, NMT took a huge leap forward in accuracy [1]. It uses a deep neural network to process huge amounts of data, and is primarily dependent on training data, from which it learns.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of English–Slovak Neural and Statistical Machine Translation

et al. 2021

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This study is focused on the comparison of phrase-based statistical machine translation (SMT) systems and neural machine translation (NMT) systems using automatic metrics for translation quality evaluation for the language pair of English and Slovak. As the statistical approach is the predecessor of neural machine translation, it was assumed that the neural network approach would generate results with a better quality. An experiment was performed using residuals to compare the scores of automatic metrics of the accuracy (BLEU_n) of the statistical machine translation with those of the neural machine translation. The results showed that the assumption of better neural machine translation quality regardless of the system used was confirmed. There were statistically significant differences between the SMT and NMT in favor of the NMT based on all BLEU_n scores. The neural machine translation achieved a better quality of translation of journalistic texts from English into Slovak, regardless of if it was a system trained on general texts, such as Google Translate, or specific ones, such as the European Commission’s (EC’s) tool, which was trained on a specific-domain.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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Evaluation of English–Slovak Neural and Statistical Machine Translation

et al. 2021

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“…The main idea of this model is to add the alignment mechanism to the basic concept of encoder-decoder for English-French translation. This idea has been developed further in two Arabic dialect translation systems [ 9 ]. The first system, dialectal translation to a standard language (D2SLT), is based on the attentional sequence-to-sequence learning model.…”

Section: Neural Machine Translationmentioning

confidence: 99%

Algerian Dialect Translation Applied on COVID-19 Social Media Comments

Slim

Melouah

Faghihi

et al. 2020

Artificial Intelligence and Renewables Towards an Energy Transition

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This work is part of a study on the propagation of misinformation about COVID-19 and its impact on Algerian society. It studies the problem of Algerian dialect translation applied to COVID-19 social media communications. The proposed system begins by filtering messages to identify comments that talk about COVID-19. Then, COVID-19 texts are translated from the Algerian dialect to formal standard Arabic. The filtering process is based on the long short-term memory (LSTM) model. The translation process is based on the embedding-GRU model. Experimental results give precision rates of about 99.98% in the filtering process and about 97.56% in the translation process. The achieved BLUE score is 22.10.

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“…For several reasons, these issues are more complicated for Arabic content. Examples of these reasons include the prevalent use of dialectal Arabic (DA) and its grave deviations from modern standard Arabic (MSA) [7]. Another reason is the common use of a non-standard romanized way of writing Arabic words known as Arabizi.…”

Section: Introductionmentioning

confidence: 99%

Atar: Attention-based LSTM for Arabizi transliteration

Talafha

Abuammar

Al‐Ayyoub

2021

IJECE

Self Cite

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A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present Atar, an attention-based encoder-decoder model for Arabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49).

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Unsupervised dialectal neural machine translation

Cited by 31 publications

References 9 publications

Evaluation of English–Slovak Neural and Statistical Machine Translation

Evaluation of English–Slovak Neural and Statistical Machine Translation

Algerian Dialect Translation Applied on COVID-19 Social Media Comments

Atar: Attention-based LSTM for Arabizi transliteration

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