Crisis Machine Translation: A Linguistic Review of Rendering COVID 19 Terms into Arabic

REFERENCES

Al-Awadhi A.M., Alsaifi K., Al-Awadhi A., Alhammadi S., (2020). Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns. Journal of Behavioral and Experimental Finance. 27 doi: 10.1016/j.jbef.2020.100326.

Alhasan, R. F., Amar, K. Abushaban, H. (2023). Challenges of Translating COVID -19 Terminology into Arabic: A Study of Machine Translation and Linguistic Means. European Journal of Multilingualism and Translation Studies, 3(1).

Almahasees, Z. M., AlBudairi, Y. (2021). Evaluation of Google Translate in Rendering English COVID -19 Texts into Arabic. Journal of Language and Linguistic Studies, 17(4).

Al-Qahtani, A. M, Almazrou, S, Alaweet, R. M., Almalky, Z. S.Al Ghamdi, S. (2021). Impact of COVID-19 on Public Knowledge and Attitudes Toward Participating in Clinical Trials in Saudi Arabia: A Cross-Sectional Study. National Library of Medicine. Retrieved from

https://pubmed.ncbi.nlm.nih.gov/34285565/

Amar, K. Abushaban, H. Alhasan, R. F., (2023). Challenges of Translating COVID -19 Terminology into Arabic: A Study of Machine Translation and Linguistic Means. European Journal of Multilingualism and Translation Studies, 3(1).

Atwany, H., Rabih, N., Raj, B., & Waheed, A. (2024). Arabic Dialect to MSA Translation. OSCAT, 1(12). Retrieved from:

https://aclanthology.org/2024.osact-1.12/

Darwish, S. (2022). Translation – Adaptation and Transcreation in Netflix English Epi-titles and their Arabic Equivalents (2016 – 2021): A Pragmatic Socio-cultural and Socio-cognitive Approach. OPDE, 80(1). Retrieved from:

https://opde.journals.ekb.eg/article_282204.html

Giaber, J., & Sharkas, H. (2021). Rendering Translation Studies Terminology from English into Arabic: Identification of Methodology and Assessment of Adequacy. International Journal of Arabic-English Studies, 21(2). https://doi.org/10.33806/ijaes2000.21.2.7

Halimah, A. (2015). Teaching Translation of Poetry: A Collaborative Approach. English Language and Literature Studies, 5(1). https://doi.org/10.5539/ells.v5n1p26 Hassan, S. (2020). COVID-19 Translation Challenges: A Case Study of Arabic Translators. Translation Studies, 13(2), 189-208.

Kaur, K. & Singh, P. (2014). English to Punjabi Transliteration using Orthographic and Phonetic Information. In Proceedings of the 11th International Conference on Natural Language Processing, pages 315–320, Goa, India. NLP Association of India.

Lou, X. (2021). Translation in the Time of COVID-19. Asia Specific Translation and Intercultural Studies, Retrieved from https://doi.org/10.1080/23306343.2021.1903183

Mahadin, D. K. & Olimat, S. N. (2022). Jordanian Translators’ Use of Machine Translation and Glossary of COVID-19 Terminology with Reference to Arabic. New Voices in Translation Studies 26 (2022)

Perez, A. (2020,09,08). Pandemic popularizes a plethora of words, phrases. News@TheU.

https://news.miami.edu/stories/2020/09/pandemic-popularizes-a-plethora-of-words,-phrases.html

Perez, L., Garcia, M., & Hernandez, R. (2022). The impact of localization on patient understanding of COVID-19 information. Journal of Medical Internet Research, 24(5), e28119. Pym et al, 2022

Roussis, D. G. (2022). Building End-to-End Neural Machine Translation Systems for Crisis Scenarios: The Case of COVID-19. NATIONAL AND KAPODISTRIAN UNIVERSITY OF ATHENS

The Saudi Ministry of Health: What do you need to know if you get infected with COVID-19? A comprehensive awareness guide regarding being infected, coming in contact with infected individuals, and about recovering from COVID-19 (2020)

World Health Organization. (2022, January 19). Coronavirus disease (COVID-19) advice for the public: Mythbusters. Retrieved from:

https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public/myth-busters

World Health Organization. (2022, May, 24). Chatbots against COVID-19: Using chatbots to answer questions on COVID-19 in the user’s language. Retrieved from:

https://www.who.int/news-room/feature-stories/detail/scicom-compilation-chatbot

World Health Organization. (2023, December 22). A report about Coronavirus disease (COVID 19). Retrieved from:

https://www.who.int/health-topics/coronavirus#tab=tab_1

INTRODUCTION

COVID 19 Pandemic

In 2020, COVID 19, the pandemic that emerged a year before in China, instantly swept the whole world. According to the World Health Organization (WHO), it is a very contagious disease “caused by the SARS-CoV-2 virus” (World Health Organization, 2023).  A lockdown took place all over the globe, and citizens were forcedly isolated to stop the spread of the disease. Consequently, the ‘cascading effects’ of such a crisis stroke all aspects of human life (Mahadin & Olimat, 2022). At its early stage, COVID 19 and its relevant details were merely a mystery where many people died and others panically tried to survive. Shortly, it affected transportation especially the flight system, employment, cultural aspects, economics, and education.

The COVID-19 pandemic had a profound effect on many aspects of life, especially in the areas of communication and translation. There was an unprecedented wave of information about the virus that spread rapidly through various media outlets around the world. As Tedros Adhanom, the Director-General of the World Health Organization, stated, “we’re not just fighting an epidemic; we’re fighting an infodemic” (WHO, 2020). Translators and interpreters worked diligently to relay information from English into numerous languages. Like medical and social service workers, both human translators and machine translation systems faced numerous challenges in finding appropriate equivalents for newly coined terms in English (Alhasan et al., 2023).

Crisis Machine Translation

The crucial role played by Machine Translation in crisis situations lies in the fact that it can easily facilitate communication among different people affected by the crisis. Machine Translation has been utilized in order to “accelerate the speed in which relief can be provided” (Roussis, 2022). During natural disasters such as earthquakes, the affected people and the relief workers do not always speak the same language. It follows that a kind of misunderstanding or miscommunication may occur and hence the crisis can be accentuated. Machine Translation can help provide speed response to the emergency and rapid assistance and aid to the disaster individuals. One example was the role of MT in the crisis of Heiti earthquake in 2010. It is also worth mentioning the efforts exerted by Translators Without Borders and INTERACT (INTERnAtional network on Crisis Translation).

Another way Machine Translation can also help in crisis situations is their integration into multilingual hotlines. During such crises, information as well as assistance and support are usually provided to affected individuals by setting up hotlines for different languages. Many people can be rescued by providing them guidance and support via hotlines, and MT facilitates this task.

Machine Translation acted as a bridge of communication during COVID 19 pandemic. Facing the crisis, “the dissemination of pandemic-related information to the whole world ranked as the top priority” (Lou, 2021). There emerged what can be called anti-COVID 19- translation as inevitable means to announce news about the fatal virus. MT systems were used by Chinese translators for the sake of vast rapid translation of pandemic related materials into different languages. In their article about Machine Translation and public health information, Pym et al pointed out how MT was highly reliable in supporting people to survive COVID 19 in Catalonia. GenCat is the most significant website that used to provide multilingual aid during the crisis. It “invites speakers of Spanish, English, and French to select their language and then see the page as translated by Google Translate” (Pym et al, 2022).

LITERATURE REVIEW

COVID 19 CHALLENGES TO ARABIC TRANSLATORS

COVID 19 pandemic imposed a great challenge to translators of all languages involving Arabic translators working in the frontlines. They had to transfer accurate meaning of new pandemic-related terminologies and ensure accurate information to Arab people. As the pandemic broke out, some new terminologies emerged and started to circulate in the announcements or declarations made by WHO as well as the reports, news, social media, and all other types of communication worldwide. According to Igorevna (2020), some new medical terms may be problematic due to their synonymity and interconnectivity. As such, translators had to work rapidly to find appropriate equivalents for the new terminologies; equivalents that transfer the correct meaning on all the linguistic and cultural levels of Arabic language. Some examples of these pandemic-related terminologies that were challenging to Arabic translators include ‘social distance’.  Stated otherwise, translating ‘social distance’ into the Arabic equivalent التباعد الاجتماعي may create a sense of ambiguity since it provokes the meaning of cutting the social interaction rather than the physical distance.

Another challenge to Arabic translators was the complex scientific terms and medical concepts. Montalt and Gonzalez Davies (2007) argue that targeting the sub-field of medical translation varies from newspaper reports and advertisements to pharmaceutical and informative leaflets. The target readers of these texts are also quite different. During normal times, this does not create a real challenge since medical and scientific terms are being tackled by specialists who understand the relevant concepts. However, during COVID 19 time, it was necessary for even laymen to understand such terms despite their ignorance of any medical or scientific background. Translators then had to create Arabic equivalents that keep the correct scientific and medical concepts along with the simplicity with which any ordinary people can fully understand the delivered message.

It is worth noting that cultural and dialect-related factors imposed another challenge to Arabic translators. Arab people do not speak Arabic language with the same dialect, and they have different cultural backgrounds depending on the country they come from. According to Atwany, et al., (2024), the focus of MT systems has largely been on Modern Standard Arabic (MSA). Consequently, these systems struggle to capture the differences inherent in dialects. As such, achieving accurate translation remains paramount. Translators then had to consider these factors while transferring the meaning of the new terminologies. They had to select the equivalents that ‘resonate’ with the culture and dialect of their different target audiences throughout Arab countries.

Time deadlines formed another kind of challenge. Translators had to provide accurate information in Arabic within a very limited time. This was due to the rapid spread of the virus and hence the rapid spread of its relevant flow of information being communicated everywhere. Machine Translation had made a great achievement in this respect. As stated by Mahadin & Olimat (2022), MT systems, Translation Memory, Cat tools, and electronic dictionaries can expedite translation process in crisis and quickly provide information to a broader audience. The use of MT systems accelerated the production of accurate equivalents and maintained the time deadlines.

Remote translation services were highly required during the pandemic for safety precautions as travelling and all kinds of face-to-face meetings were restricted (Alhasan et al, 2023). This was another necessity of relying on technology and MT systems for facilitating the work and cooperation among translators.

The present research is confined to show some of the linguistic challenges that faced Arabic translators during COVID 19 pandemic. It also provides how these challenges were solved and how newly coined terminologies were accurately translated into Arabic. The role of MT systems is also tackled. An evaluation of the translation of the new terminologies produced by MT systems is provided as well.

METHODOLOGY

The present research adopts a descriptive and analytical approach examining the translation of COVID 19 terms from English into Arabic using MT systems. A group of words, whether newly coined or widely used during the pandemic, were selected based on their frequent use in scientific or medical context as well as in the media. Three widely used MT systems were selected for the analysis: Google Translate, Reverso, and Microsoft Translator (Bing). These systems were selected due to their popularity and accessibility not only for professionals but also for the public. The procedures start by inserting the selected terms into the three MT systems to receive their Arabic translations. The translations provided were analyzed for accuracy in the context of the COVID 19 pandemic. the Dictionary of COVID-19 Terms published by the Arab League Educational, Cultural, and Scientific Organization (ALECSO) served as the benchmark for accurate translation.

THE ANALYSIS

1. THE SIGNIFICANCE OF LOCALIZATION

During the pandemic, the process of localization assumed a pivotal role in the adaptation of newly established terminology associated with the pandemic into various specific languages. Furthermore, it played an integral role in enhancing understanding despite the existence of cultural impediments (Perez et al., 2022). Nonetheless, as posited by Rizzo and Amato (2021), this endeavor proved to be intricate owing to the rapid proliferation of the pandemic and the extensive volume of information necessitating translation. Translators were compelled to remain abreast of this incessant influx of information and emerging terminology. They also depended significantly on technological advancements, particularly machine translation systems, to expedite this undertaking (Rizzo and Amato, 2021 as cited in Alhasan et al., 2023). By utilizing a diverse array of linguistic strategies and machine translation methodologies, public awareness regarding the pandemic was significantly elevated, given that precise translations were generated (Alhasan, 2023). Arabization played a crucial role in helping Arab natives to understand public health measures. Among the examples of these measures were the restrictions imposed on travel, the lockdowns, and the quarantine measures. The governments and authorities in Arab countries could control the virus and save their people by adopting Arabization approach ((Amar et al., 2023)). By adapting content and communication strategies to Arabic language and culture Arab authorities ensured that all segments of Arab communities could effectively understand public health measures. Arab governments could leverage Arabization to manage pandemic effectively. For example, simple clear Arabic terms that avoided complex medical jargon were used to ensure the public health guidelines (e.g. mask-wearing, and hand hygiene). Arab authorities launched media campaigns on TV channels and social media platforms. To ensure that both urban and rural populations can access and fully comprehend the media messages, all the complex English medical terms were adapted into simple Arabic. Arabic posters and flyers were also displayed in public spaces explaining COVID 19 prevention measures. Due to Arabization, Arab populations could fully comprehend and respond to public health messages such as the importance of vaccination, using facial masks, and adhering to social distancing (Al-Qahtani et al., 2021).

• Linguistic Means to Localization

The adaptation of texts from one language to another can be achieved through different linguistic means such as direct translation and transliteration. Another significant method is transcreation where the source content is adapted to resonate culturally and idiomatically with the target audience (Darwish, 2022). During COVID 19 pandemic, some newly coined terminologies had to be localized into the Arabic language. They ranged from being familiar to strange and sometimes weird terms (Al-Awadhi, 2020). These terms can be divided into three categories as follows:

• General terms: They are familiar terms that were generally used before the pandemic. However, they became more common and used on a daily basis after COVID 19 outbreak. Examples of these terms are border closure, hygiene, sterilization and virus. (Al-Awadhi, 2020)
• Medical terms: Terms and expressions that were used during the pandemic by individuals working in the medical domain only. Examples of these terms are quarantine, self-isolation, community spread, transmission, incubation period, and pandemic. (Perez, 2020)
• Neologies: New terms that appeared for the first time and were and were related to the pandemic. Examples of these new terms are covidiot and coronial. (Al-Awadhi, 2020)

The following is a summary of some linguistic means that were used to translate and adapt the pandemic related terms into Arabic:

• Transliteration

Unlike translation, the process of transliteration does not aim to transfer the meaning of the text from the source language to its appropriate equivalent in the target language. Transliteration merely represents the source language words using the phonetic and orthography of the target language (Kaur & Singh, 2014). It bridges the gap between languages especially in terms of translating technical and medical terms where there may not be full appropriate equivalents that transfer the meaning at all levels of languages along with the cultural connotations. During the pandemic, some terminologies appeared and were transferred into Arabic using the method of transliteration. The following table includes some of these terminologies.

Table (1): Transliteration of terminologies

It should be noted here that the term ‘Coronavirus’ has another method of translation into Arabic. It is the direct translation of its meaning into ‘المرض التاجي’ or ‘المرض الوبائي’ as stated by Alhasan et al. (2023). Likewise, SARS has an Arabic equivalent though it is not an acronym. It is translated into ‘متلازمة الالتهاب الرئوى الحاد’.

• Direct translation

Direct translation refers to the common process of transferring the meaning from the source language to the target language without any interpretation or adaptation. The following table includes some examples of expressions translated into Arabic using the method of direct translation.

Table (2): Direct Translation of Terminologies

• Literal translation

In their article about the translation of COVID 19 terminology, Halimah & Almakhyatah stated some inaccurate translations of COVID 19 terms into Arabic as a result of adopting the literal translation method (Halimah & Almakhyatah, 2023). They investigate some expressions made by the Saudi Ministry of Health (SMOH) in 2020. One of these examples is the inaccurate literal translation of the English term ‘isolation’ into the Arabic equivalent ‘الحجر’.

Table (3): Literal Translation of Terminologies

Isolation and quarantine are both precautionary actions against COVID 19. According to SMOH, ‘home isolation’ is "staying at home under observation for those who have symptoms regardless of the laboratory test". The same organization defines ‘home quarantine’ as “staying at home under observation for those who had been in contact with a confirmed case and have no symptoms regardless of the laboratory test" (SMOH, 2020:7). However, SMOH made an inaccurate equivalent of the English term ‘isolation’ through a literal translation of the word into ‘الحجر’ whereas it should have been ‘العزل’ to match the appropriate meaning of the word.

• Derivation

Derivation is another method of translating COVID 19 terminologies. It allows for the morphological development of some Arabic words to covey the meaning of their equivalent in English (The Unified Medical Dictionary:1037). The following examples illustrate this method.

Table (4): Derivation

To find an equivalent of the term ‘intubation’ which means ‘placing the tube’, Arabic translators derived the word ‘تنبيب’ from the syntactic form ‘taf’il’. The two other words, ‘إماتة’ and ‘مراضة’, were also derived from the syntactic form of ‘fi’ala’ and ‘fa’ala’ respectively.  This method sometimes prevents the production of mistranslation. For example, the Unified Medical Dictionary (UMD) translates the word ‘outbreak’ and its plural ‘outbreaks’ into ‘فاشية’ and ‘فاشيات’. This is a mistranslation of the two words not only because it does not convey the appropriate Arabic equivalents, but it has a negative cultural connotation in Arabic as well. The words ‘فاشية’ and ‘فاشيات’ refer to women who believe in fascism. In this way, the best method to translate ‘outbreak’ is to tend to derivation (Giaber & Sharkas, 2021). Thus, the appropriate equivalents would be ‘تفشي’ and the derived plural ‘تفشيات’ that follows the Arabic syntactic method of forming the plural.

• Cultural adaptation

Adapting the translated content to fit the cultural connotations of the target audience is called cultural adaptation in translation. It is vital as it ensures that the target audience understands the content linguistically and culturally. Some COVID 19 terms were translated in such a way that they indicated negative cultural connotations for the Arab audience. Among these terms are the following examples.

Table (5): Cultural Adaptation

Despite the above-mentioned translation is linguistically accurate or not, they all have negative connotations in Arabic culture. For the first term, social distance’التباعد الاجتماعي, it indicates cutting social ties among people and relatives. The term ‘herd immunity’مناعة القطيع metaphorically describes people as a large group of animals. The third term, ‘lockdown’, means ‘ban’ and ‘closure’ which both indicate the sense of prohibition. The last term, ‘elbow bump’, as stated by Amar et al., indicates ‘colliding’, and this is opposing peace and greeting. It is better translated into ‘elbow shake’ ‘السلام بالكوع’ (Amar et al., 2023). The cultural connotation was considered in translating terms such as ‘face mask’ which literally refers to ‘قناع الوجه’. However, it was translated and commonly used by Arab people as ‘كمامة’ (Alhasan et al., 2023).

• Abbreviation

COVID 19 pandemic resulted in the emergence of some abbreviations of the terminologies that were commonly used during the pandemic. These abbreviations cannot be transferred into Arabic abbreviations by any means. As such, they were written in English abbreviated letters and accompanied by some explanation. The following are some examples of COVID 19-related abbreviation.

Table (6): Abbreviation

• Coinage and neologism

In the context of COVID 19 pandemic, new terminologies were coined or entirely created, and they were related to that specific incident. Other terms were created by combining existing words. This process is called neologism. The following are some examples of such new terms.

Table (7): Coinage and Neologism

2. MACHINE TRANSLATION OF COVID 19 TERMINOLOGIES

MT systems were highly utilized during COVID 19 pandemic for translating the newly coined terminologies. It is not always reliable to accept the production of MT systems without a review made by human translators especially when dealing with terminologies that are coined in the source language to express different cases and entities that have appeared in a specific occasion. In the present research, three MT engines are used to translate the newly coined terms into Arabic; namely, Google Translate, Reverso, and Microsoft Translator Bing. The aim is to investigate the ability of these MT systems to produce accurate Arabic translation of Covid 19 newly coined terms. The following table includes some of the major terminologies that were commonly used during the pandemic. The English terminologies are rendered into Arabic using the three MT system and followed by a brief discussion of the resulted translations.

Table (8): MT Systems Translation of Terminologies

As shown in the table, some terminologies are appropriately translated by at least one of the three systems. The term ‘lockdown’ is translated by Google as ‘إغلاق’, and by Reverso as ‘إغلاق أو حظر’, which can be accepted and understood. However, Bing produces more accurate translation of this term; ‘التزام المنزل’ which seems more accurate in terms of the pandemic context. The term ‘quarantine’ appears in different terminologies according to whether it stands by itself or as part of another terminology. Standing by itself, ‘quarantine’ is properly rendered into the Arabic equivalent ‘حجر صحي’ by the three systems. It is also correct in other terminologies such as ‘quarantine haircut’ ‘قصة شعر الحجر الصحي’ as produced by Google and Reverso. It also keeps the same proper translation in ‘quarantine and chill’ despite of the wrong translation of the whole phrase made by the three systems. The newly coined word ‘infodemic’ is not recognized as an English word by Google and Reverso. So, it is copied using the same English letters by Google as ‘infodemic’ and by Reverso as ‘infodemy’. However, Bing produces the most appropriate translation of the term as ‘وباء المعلومات’. Bing and Reverso seem more successful than Google in rendering the term ‘contact tracing’ into Arabic. The term is not recognized by Google in the COVID 19 context, so it is translated as ‘تتبع الاتصال’ where ‘contact’ is understood by Google literally as ‘تتبع’. On the other side, Reverso translates the term as ‘متابعة مخالطي المرضى’ which seems more explanatory and appropriate than Google’s translation. Bing also creates the successful translation of the term as ‘تتبع المخالطين’ where the COVID 19 context is considered while translating the term.

 MT systems were highly relied on during the pandemic for producing translations in different languages in no time. The World Health Organization (WHO) deployed MT systems to manage the huge content across languages. For example, WHO’s COVID – 19 Mythbusters page was available in different languages and serving diverse audiences. (WHO, 2022, 01, 19). Another example was the partnership between WHO and tech companies such as Google, Facebook, and WhatsApp to disseminate Covid 19 information. WHO and WhatsApp, for instance, created a chatbot to provide information and reply to any inquiries, and it applies MT tools to translate into different languages. (WHO, 2022, 05, 24) some neologies need considerable review by human translators. This appears in the translation of some terminologies that were newly coined in the source language (English) for the first time during the pandemic. These terminologies need more explanation in the target language to be understood by the target audience. Examples of these new terminologies are presented in the table. The term ‘coronial’, for instance, is translated by Google and Reverso as ‘تاجي’ which is not the proper translation of the word as used during the pandemic. Bing does not provide any Arabic translation of the term at all. It uses the transliteration technique instead, ‘كورونيال’. However, ‘coronial’ is a newly coined term that refers to babies who were born during COVID 19 pandemic. So, the successful translation of the term should be ‘مواليد كورونا’.

Although the term ‘quarantine’ is successfully translated by the three systems, the new term ‘quarantine and chill’ is not properly translated by any of them. Google, Reverso, and Bing create the same mistranslation of ‘chill’ as ‘البرد’, so the whole expression is not understood by the Arabic audience. The correct translation of the whole term should be ‘الحجر الصحي والاسترخاء’ since ‘chill’ refers to ‘relaxing’ in this context rather than ‘being cold’.

Another neology is ‘social bubble’. The term refers to the group of people with whom you can spend time or get together during the pandemic as they are safe and do not carry the virus. The word ‘bubble’ in this context means a group of people. however, Google, Reverso, and Bing deal with ‘bubble’ as a ball of air or gas that appears in liquids. So, the three systems mistranslate ‘bubble’ into ‘فقاعة’. The most accurate translation may be ‘مجموعات معزولة’ as it considers the indication that they are groups of people rather than balls in liquids.

During the pandemic, people were kept for long hours in their houses. So, they tend to use the internet and applications for the purpose of social gathering. One of the most widely used application then was Zoom. Since people got tired of using Zoom for a long time, a newly coined term appeared; ‘Zoom fatigue’, to express getting tired of using Zoom for a long time. However, the indication of the term is not considered by MT systems. So, it is translated into ‘التعب التكبير’ by Google and ‘إجهاد التكبير’ by both Reverso and Bing. As shown, the three machines consider Zoom as moving from long shot to small close of a camera or a text rather than the name of the application.

According to Almahasees et al., Google translate provides incorrect translation of the acronyms (Almahasees et al., 2021). One of the acronyms related to and widely used during COVID 19 pandemic was PCR. It stands for Polymerase Chain Reaction. It is a test to detect corona virus. As shown in the table, none of the three MT systems can produce any Arabic equivalent of ‘PCR’. They all translate ‘PCR test’ as ‘اختبار PCR’ keeping the acronyms as they are in the English letters. 

Some coined terminologies came up for the first time during COVID 19 pandemic and were made up of the merge of two English words. Among the examples of such terminologies are ‘covexit’, ‘coronacation’, and ‘maskne’. For the term ‘covexit’, it is made up of ‘covid’ and ‘exit’, and it indicates the gradual relaxation and removing the restrictions that were imposed due to COVID 19 pandemic. Google and Reverso do not provide any translation of the term and copy and paste it into the Arabic language. As Bing adopts the method of transliteration, it provides ‘كوفيكست’ as the translation of the term. The suggested translation could be ‘الخروج من الجائحة’ which best describe the meaning of the coined term. By the same token, the term ‘maskne’ indicates the acne created by the mask as being put on for a long time. The term is made up of two English words: ‘mask’ and ‘acne’. However, none of the three systems could recognize the coined term. Google rendered it into ‘com.maskne’, Riverso into ‘maskne’, and surprisingly, Bing into ‘مسكن’ which seems as transliteration of the term into Arabic, but at the same time, could be ‘maskan’ or ‘house’. The best suggestion for the term is ‘حبوب الكمامة’. Another example of coined terminologies is ‘coronacation’. It is made up of two words: ‘corona’ and ‘vacation’. It indicates the vacation due to getting coronavirus. The three systems render the term into Arabic as ‘تتويج’ without any consideration of the coinage made. The best suggested translation could be ‘أجازة كورونا’.

In this way, as MT systems played a vital role in the rapid transfer of information during COVID 19 pandemic, they always needed human revision to produce the most appropriate translations in Arabic.

CONCLUSION

COVID 19 was a world pandemic that imposed challenges to human translators and MT systems as well. Due to the massive flow of information being circulated all over the world, translation played a crucial role in delivering this information. It was also challenging to keep up with the rapidity of the emergence of information. The present research reviews newly coined terms in the English language and how these terms were translated into Arabic by the help of MT systems. A review of linguistic means of localizing these terms is made, then, three MT engines are used to translate the newly coined terms into Arabic; namely, Google Translate, Reverso, and Microsoft Translator Bing. The aim is to investigate the ability of these MT systems to produce accurate Arabic translation of Covid 19 newly coined terms.

The results show that there were three kinds of terminologies related to COVID 19 pandemic. General terms that existed before the pandemic came to be used on a wider scale during the pandemic.  Medical terminologies that were used by doctors, nurses and all the medical staff were focused on during the pandemic. The third type of terminologies were the newly coined terms that appeared for the first time during the pandemic. There is a difference in accuracy among the three MT systems, however, they were reliable in translating a massive amount of information to different languages all over the world in no time. The results show that MT systems could translate all terms that were related to the pandemic. However, they need a little help from human translators for a better output, especially with newly coined or culture – related terminologies.