Facile Solutions to the Problems Associated with Chemical Information and Mathematical Symbolism While Using Machine Translation Tools

2020 ◽  
Vol 60 (7) ◽  
pp. 3423-3430
Author(s):  
M. Farooq Wahab ◽  
Sonia Zulfiqar ◽  
Muhammad Ilyas Sarwar ◽  
Ingo Lieberwirth
Keyword(s):  
Machine Translation ◽  
Chemical Information ◽  
Translation Tools ◽  
Mathematical Symbolism

scholarly journals Translation, from Pen-and-Paper to Computer-Assisted Tools (CAT Tools) and Machine Translation (MT)

Proceedings ◽  
2020 ◽  
Vol 63 (1) ◽  
pp. 56
Author(s):  
Bianca Han
Keyword(s):  
Machine Translation ◽  
Industry 4.0 ◽  
New Technology ◽  
Project Manager ◽  
Computer Assisted ◽  
Translation Process ◽  
Translation Tools

This paper reflects the technology-induced novelty of translation, which is perceived as a bridge between languages and cultures. We debate the extent to which the translation process maintains its specificity in the light of the new technology-enhanced working methods ensured by a large variety of Computer-Assisted Translation (CAT) and Machine Translation (MT) tools that aim to enhance the process, which includes the translation itself, the translator, the translation project manager, the linguist, the terminologist, the reviewer, and the client. This paper also hints at the topic from the perspective of the translation teacher, who needs to provide students with transversal competencies that are suitable for the digital area, supported by the ability to tackle Cloud-based translation tools, in view of Industry 4.0 requirements.

scholarly journals Recursive Machine Translation

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Joshua Evans
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Literary Culture ◽  
Literary Texts ◽  
Literary Value ◽  
Translation Tools ◽  
Methodological Reflection

Machine translation tools such as Google Translate are at best seen as useful approximators, rather than offering any literary potential. In this experiment and short methodological reflection, I use Google Translate to recursively translate Austrian poet Georg Trakl’s celebrated WWI poem, ‘Grodek’, between German and English, until the two versions stabilise. I am attentive to places in which the poem and its renderings are simplified and/or literary value may be lost, but also places in which new or unexpected renderings emerge. This is a preliminary foray, but I propose that the method of recursive machine translation offers a new way to explore the translation of literary texts—a timely proposal, given the increasing applications of computer programmes and machine learning both within the humanities and throughout wider literary culture.

scholarly journals Evaluating Machine Translations from Arabic into English and Vice Versa

2017 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Riyad Al-Shalabi ◽  
Ghassan Kanaan ◽  
Huda Al-Sarhan ◽  
Alaa Drabsh ◽  
Islam Al-Husban
Keyword(s):  
Machine Translation ◽  
Word Order ◽  
Solution Structure ◽  
Sentence Length ◽  
Third Party ◽  
Abstract Machine ◽  
Translation Tools ◽  
Index Terms ◽  
Word Selection ◽  
Accuracy Degree

Abstract—Machine translation (MT) allows direct communication between two persons without the need for the third party or via dictionary in your pocket, which could bring significant and per formative improvement. Since most traditional translational way is a word-sensitive, it is very important to consider the word order in addition to word selection in the evaluation of any machine translation. To evaluate the MT performance, it is necessary to dynamically observe the translation in the machine translator tool according to word order, and word selection and furthermore the sentence length. However, applying a good evaluation with respect to all previous points is a very challenging issue. In this paper, we first summarize various approaches to evaluate machine translation. We propose a practical solution by selecting an appropriate powerful tool called iBLEU to evaluate the accuracy degree of famous MT tools (i.e. Google, Bing, Systranet and Babylon). Based on the solution structure, we further discuss the performance order for these tools in both directions Arabic to English and English to Arabic. After extensive testing, we can decide that any direction gives more accurate results in translation based on the selected machine translations MTs. Finally, we proved the choosing of Google as best system performance and Systranet as the worst one.  Index Terms: Machine Translation, MTs, Evaluation for Machine Translation, Google, Bing, Systranet and Babylon, Machine Translation tools, BLEU, iBLEU.

scholarly journals A Survey on Hybrid Machine Translation

2020 ◽  
Vol 184 ◽  
pp. 01061
Author(s):  
Anusha Anugu ◽  
Gajula Ramesh
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Literature Survey ◽  
Neural Machine Translation ◽  
Translation Tools ◽  
Translation Techniques ◽  
Hybrid Machine ◽  
Hybrid Machine Translation ◽  
Translation Systems ◽  
Evaluation Techniques

Machine translation has gradually developed in past 1940’s.It has gained more and more attention because of effective and efficient nature. As it makes the translation automatically without the involvement of human efforts. The distinct models of machine translation along with “Neural Machine Translation (NMT)” is summarized in this paper. Researchers have previously done lots of work on Machine Translation techniques and their evaluation techniques. Thus, we want to demonstrate an analysis of the existing techniques for machine translation including Neural Machine translation, their differences and the translation tools associated with them. Now-a-days the combination of two Machine Translation systems has the full advantage of using features from both the systems which attracts in the domain of natural language processing. So, the paper also includes the literature survey of the Hybrid Machine Translation (HMT).

scholarly journals Translator Education at a Crossroads:the Impact of Automation

2019 ◽  
Vol 64 (1) ◽  
pp. 103-121 ◽  
Author(s):  
Begoña Rodríguez de Céspedes
Keyword(s):  
Machine Translation ◽  
New Technologies ◽  
Service Providers ◽  
Computer Assisted ◽  
Daily Lives ◽  
Translation Tools ◽  
Technological Competence ◽  
The Future ◽  
The Impact ◽  
Translator Education

Abstract Automation is affecting all spheres of our daily lives and humans are adapting both to the challenges that it poses and the benefits that it brings. The translation profession has also experienced the impact of new technologies with Language Service Providers adapting to changes (Presas/Cid-Leal/Torres-Hostench 2016; Sakamoto/Rodríguez de Céspedes/Evans/Berthaud 2017). Translation trainers are not oblivious to this phenomenon. There have indeed been efforts to incorporate the teaching of digital translation tools and new technologies in the translation classroom (Doherty/Kenny/Way 2012; Doherty/Moorkens 2013; Austermühl 2013; O’Hagan 2013; Gaspari/Almaghout/Doherty 2015; Moorkens 2017) and many translation programmes in Europe are adapting their curricula to incorporate this necessary technological competence (Rothwell/Svoboda 2017). This paper reflects on the impact that automation and, more specifically machine translation and computer assisted tools, have and will have on the future training of translators and on the balance given by translation companies to language and technological skills.

scholarly journals Machine Translation and Technicalities of Website Localization

2019 ◽  
Author(s):  
Hamza Ethelb
Keyword(s):  
Machine Translation ◽  
Special Focus ◽  
Cultural Issues ◽  
Localization Task ◽  
Translation Tools ◽  
Technical Issues ◽  
Increasing Demand ◽  
Shed Light ◽  
Website Localization

Machine translation tools are widely used by companies. The tools are on an increasing demand. Translators need to equip themselves with the knowledge and the mastering of these tools. This study explores two machine translation tools involved in website localization. These tools are Alchemy Catalyst and Trados Tageditor. The study adopts an evaluative methodology to shed light on the intricacies and technicalities of these two localization tools. It discusses some of the cultural issues that localizers come across in the process of localization. In addition, it delves into the technical issues, mainly focusing on localizing into Arabic with a special focus on string, text, lexis, and orthography. The study concludes that the process of localization requires teamwork and involvement of computer engineers, and both localization tools are valuable in achieving a localization task.

scholarly journals Evaluating Urdu to Arabic Machine Translation Tools

2017 ◽  
Vol 8 (10) ◽  
Author(s):  
Maheen Akhter ◽  
Sahar Noor ◽  
Muhammad Ramzan ◽  
Hikmat Ullah
Keyword(s):  
Machine Translation ◽  
Translation Tools

scholarly journals A Review of Machine Translation Tools: The Translation’s Ability

2021 ◽  
Vol 16 (1) ◽  
pp. 162-176
Author(s):  
Tira Nur Fitria
Keyword(s):  
Machine Translation ◽  
Cognitive Abilities ◽  
Active Role ◽  
Translation Tools ◽  
The World ◽  
Human Skills ◽  
Translation Machine ◽  
One Machine ◽  
Descriptive Qualitative

The objective of the research is to review the ability of online machine translator tools includes Google Translate (GT), Collin Translator (CT), Bing Translator (BT), Yandex Translator (YT), Systran Translate (ST), and IBM Translator (IT). This research applies descriptive qualitative. The documentation was used in this study. The result of the analysis shows that the translation results are different, both from the style of language and the choice of words used by each machine translation tool. Thus, directly or indirectly, whether consciously or not, each translation machine carries its characteristics. Machine translation technology cannot be separated from the active role of humans. In other words, it will always be the best choice for users to rely on expert translation rather than machine translation. But no machine translator can be as accurate as human skills in producing translation products. In particular, the field of translation is also concerned with machine translation to support the performance of translators in analyzing the diction used as an element of language. In this regard, it needs to be underlined that the existence of machine translation is an additional facility in the world of translation, not as the main means of translation because the sophistication of the machine will not be able to match the flexibility of the human brain's cognitive abilities in adjusting the translation results according to the existing context. Accurate translation is sometimes subjective, relatively often temporal. Therefore, it is permissible for translating by more than one machine translator 

scholarly journals Supporting Risk-Aware Use of Online Translation Tools in Delivering Mental Healthcare Services among Spanish-Speaking Populations

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wenxiu Xie ◽  
Meng Ji ◽  
Mengdan Zhao ◽  
Xiaobo Qian ◽  
Chi-Yin Chow ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Mental Healthcare ◽  
Healthcare Services ◽  
Likelihood Ratios ◽  
Spanish Speaking ◽  
Healthcare Settings ◽  
Learning Classifier ◽  
Translation Tools ◽  
Bayesian Machine Learning

Neural machine translation technologies are having increasing applications in clinical and healthcare settings. In multicultural countries, automatic translation tools provide critical support to medical and health professionals in their interaction and exchange of health messages with migrant patients with limited or non-English proficiency. While research has mainly explored the usability and limitations of state-of-the-art machine translation tools in the detection and diagnosis of physical diseases and conditions, there is a persistent lack of evidence-based studies on the applicability of machine translation tools in the delivery of mental healthcare services for vulnerable populations. Our study developed Bayesian machine learning algorithms using relevance vector machine to support frontline health workers and medical professionals to make better informed decisions between risks and convenience of using online translation tools when delivering mental healthcare services to Spanish-speaking minority populations living in English-speaking countries. Major strengths of the machine learning classifier that we developed include scalability, interpretability, and adaptability of the classifier for diverse mental healthcare settings. In this paper, we report on the process of the Bayesian machine learning classifier development through automatic feature optimisation and the interpretation of the classifier-enabled assessment of the suitability of original English mental health information for automatic online translation. We elaborate on the interpretation of the assessment results in clinical settings using statistical tools such as positive likelihood ratios and negative likelihood ratios.

scholarly journals Machine translation systems and guidebooks: an approach to the importance of the role of the human translator

2020 ◽  
Author(s):  
Adrián Fuentes-Luque ◽  
Alexandra Santamaría Urbieta
Keyword(s):  
Machine Translation ◽  
Computer Assisted ◽  
Working Environments ◽  
Translation Tools ◽  
Technical Translation ◽  
Complete Translation ◽  
Translation Systems ◽  
Tourist Guides

Computer-assisted translation tools are increasingly supplemented by the presence of machine translation (MT) in different areas and working environments, from technical translation to translation in international organizations. MT is also present in the translation of tourism texts, from brochures to food menus, websites and tourist guides. Its need or suitability for use is the subject of growing debate. This article presents a comparative analysis of tourist guides translated by a human translator and three machine translation systems. The aims are to determine a first approach to the level of quality of machine translation in tourist texts and to establish whether some tourist texts can be translated using machine translation alone or whether human participation is necessary, either for the complete translation of the text or only for post-editing tasks.

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