Assessing the Translation of Google and Microsoft Bing in Translating Political Texts from Arabic into English

Almahasees, Zakaryia

doi:10.18178/ijlll.2017.3.1.100

Cited by 10 publications

(10 citation statements)

References 8 publications

(6 reference statements)

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“…Almahasees [24] compared the two most popular machine translation systems, Google Translate and the Microsoft Bing translator. Both systems used statistical machine translation.…”

Section: Related Workmentioning

confidence: 99%

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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“…Almahasees [24] compared the two most popular machine translation systems, Google Translate and the Microsoft Bing translator. Both systems used statistical machine translation.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of English–Slovak Neural and Statistical Machine Translation

et al. 2021

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“…Almahasees, 2020). In most cases, translation quality looks for output clarity, adequacy, and fluency as prerequisites to determine output acceptability (Z. M. Almahasees, 2017). Translation quality requires comprehension to determine various kinds of translation equivalence and identify translation errors (Chan, 2014).…”

Section: Machine Translation Evaluation (Mte)mentioning

confidence: 99%

Evaluation of google translate in rendering English COVID-19 texts into Arabic

Almahasees¹,

Meqdadi

Albudairi

2021

JLLS

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Machine Translation (MT) has the potential to provide instant translation in times of crisis. MT provides real solutions that can remove borders between people and COVID-19 information. The widespread of MT system makes it worthy of scrutinizing the capacity of the most prominent MT system, Google Translate, to deal with COVID-19 texts into Arabic. The study adopted (Costa et al., 2015a) framework in analysing the output of Google Translate output service in terms of orography, grammar, lexis, and semantics. The study's corpus was extracted from World Health Organization (WHO), United Nations Children's Emergency Fund (UNICEF), U.S. Food and Drug Administration (FDA), the Foreign, Commonwealth & Development Office (FCDO), and European Centre for Disease Prevention and Control (ECDC). The paper reveals that Google Translate committed a set of errors: semantic, grammatical, lexical, and punctuation. Such errors inhibit the intelligibility of the translated texts. It also indicates that MT might work as an aid to translate general information about COVID-19, but it is still incapable of dealing with critical information about COVID-19. The paper concludes that MT can be an effective tool, but it can never replace human translators.

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“…MT could be evaluated manually and automatically [29], [30]. The author in [31] states manual evaluation investigates the systems' usability via human participants by means of Error Analysis […], whereas automatic evaluation examines MT outputs through the text's similarity to a referenced translation.…”

Section: Machine Translation Evaluationmentioning

confidence: 99%

Evaluation of Facebook Translation Service (FTS) in Translating Facebook Posts from English into Arabic in Terms of TAUS Adequacy and Fluency during Covid-19

Jaccomard¹,

Almahasees²,

Mohammad³

2021

Adv. sci. technol. eng. syst. j.

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The study aims to verify the capacity of Facebook Translation Service in translating English Facebook posts into Arabic in terms of two criteria: adequacy and fluency in line with the Translation Automation User Society (TAUS) scales. To ensure consistency and objectivity as recommended by TAUS, six evaluators, native speakers of Arabic and near-native speakers of English, rated the same data on each scale. The evaluators were acquainted with fluency and adequacy scales along with MT limitations and potentials. Once the corpus was uploaded and sent to the evaluators using TAUS tools, they had to assign scores online on 1-4 rating scales. Then, each report was displayed online on the TAUS reports tool. Evaluators' responses were combined in thematic categories and were calculated to obtain frequencies and percentages. The study found that FTS provided fluent output with highest percentage of the scale good equal to 3 on a scale from 1 to 4, where the output is assessed as flowing smoothly with minor linguistic errors. Moreover, FTS succeeded in generating an adequate output with the highest percentage of responses as 'most' equal to 3 on a scale from 1 to 4, where almost the full meaning of the source is deemed to be transferred in the target language. This study is useful since it highlights the role of Facebook Translation service in translating, educating the public and fighting COVID-19. Consequently, such research would encourage the use and research on the potentiality of MT and FTS in dealing with abrupt crises, such as

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Assessing the Translation of Google and Microsoft Bing in Translating Political Texts from Arabic into English

Cited by 10 publications

References 8 publications

Evaluation of English–Slovak Neural and Statistical Machine Translation

Evaluation of English–Slovak Neural and Statistical Machine Translation

Evaluation of google translate in rendering English COVID-19 texts into Arabic

Evaluation of Facebook Translation Service (FTS) in Translating Facebook Posts from English into Arabic in Terms of TAUS Adequacy and Fluency during Covid-19

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