scholarly journals Machine Translation and Technicalities of Website Localization

2019 ◽  
Author(s):  
Hamza Ethelb
Keyword(s):  
Machine Translation ◽  
Website Localization

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.

Factored Statistical Machine Translation for German-English

2018 ◽  
Vol 5 (1) ◽  
pp. 37-45
Author(s):  
Darryl Yunus Sulistyan
Keyword(s):  
Machine Translation ◽  
English Language ◽  
Statistical Machine Translation ◽  
New Model ◽  
Language Pair

Machine Translation is a machine that is going to automatically translate given sentences in a language to other particular language. This paper aims to test the effectiveness of a new model of machine translation which is factored machine translation. We compare the performance of the unfactored system as our baseline compared to the factored model in terms of BLEU score. We test the model in German-English language pair using Europarl corpus. The tools we are using is called MOSES. It is freely downloadable and use. We found, however, that the unfactored model scored over 24 in BLEU and outperforms the factored model which scored below 24 in BLEU for all cases. In terms of words being translated, however, all of factored models outperforms the unfactored model.

On (Not) Translating Lacan: Barbara Cassin's Sophistico-Analytical Performances

Paragraph ◽  
2020 ◽  
Vol 43 (1) ◽  
pp. 98-113
Author(s):  
Michael Syrotinski
Keyword(s):  
Machine Translation ◽  
Reading And Writing ◽  
The Relationship ◽  
The Way

Barbara Cassin's Jacques the Sophist: Lacan, Logos, and Psychoanalysis, recently translated into English, constitutes an important rereading of Lacan, and a sustained commentary not only on his interpretation of Greek philosophers, notably the Sophists, but more broadly the relationship between psychoanalysis and sophistry. In her study, Cassin draws out the sophistic elements of Lacan's own language, or the way that Lacan ‘philosophistizes’, as she puts it. This article focuses on the relation between Cassin's text and her better-known Dictionary of Untranslatables, and aims to show how and why both ‘untranslatability’ and ‘performativity’ become keys to understanding what this book is not only saying, but also doing. It ends with a series of reflections on machine translation, and how the intersubjective dynamic as theorized by Lacan might open up the possibility of what is here termed a ‘translatorly’ mode of reading and writing.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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