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).

Automatic Extraction of Engineering Rules From Unstructured Text: A Natural Language Processing Approach

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Xinfeng Ye ◽  
Yuqian Lu
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Cloud Manufacturing ◽  
Automatic Extraction ◽  
Web Based ◽  
Neural Machine Translation ◽  
Engineering Knowledge ◽  
Unstructured Text ◽  
Translation Techniques

Abstract Manufacturers use cloud manufacturing platforms to offer their services. The literature has suggested a semantic web-based cloud manufacturing framework, in which engineering knowledge is modeled using structured syntax. Translating engineering rules to semantic rules by human is a painstaking task and prone to mistakes. We present a scheme that treats converting engineering knowledge into semantic rules as a machine translation task and uses neural machine translation techniques to carry out the conversion.

scholarly journals A Survey of Orthographic Information in Machine Translation

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Bharathi Raja Chakravarthi ◽  
Priya Rani ◽  
Mihael Arcan ◽  
John P. McCrae
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Orthographic Knowledge ◽  
Translation System ◽  
Neural Machine Translation ◽  
Machine Translation System ◽  
Translation Methods ◽  
Traditional Approaches ◽  
Translation Systems ◽  
Different Levels

AbstractMachine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the linguistic difference and variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography’s influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts using cognate information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction.

scholarly journals Is automation changing the translation profession?

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Anthony Pym ◽  
Ester Torres-Simón
Keyword(s):  
Machine Translation ◽  
Communication Skills ◽  
Service Providers ◽  
Time Data ◽  
Market Segments ◽  
Interactive Communication ◽  
Neural Machine Translation ◽  
History Of ◽  
Translation Services ◽  
Translation Systems

Abstract As a language-intensive profession, translation is of frontline interest in the era of language automation. In particular, the development of neural machine translation systems since 2016 has brought with it fears that soon there will be no more human translators. When considered in terms of the history of automation, however, any such direct effect is far from obvious: the translation industry is still growing and machine translation is only one instance of automation. At the same time, data on remuneration indicate structural wage dispersion in professional translation services, with some signs that this dispersion may increase in certain market segments as automated workflows and translation technologies are adopted more by large language-service providers more than by smaller companies and individual freelancers. An analysis of recent changes in discourses on and in the translation profession further indicates conceptual adjustments in the profession that may be attributed to growing automation, particularly with respect to expanding skills set associated with translation, the tendency to combine translation with other forms of communication, and the use of interactive communication skills to authorize and humanize the results of automation.

SisHiTra : A Hybrid Machine Translation System from Spanish to Catalan

2004 ◽  
pp. 349-359 ◽  
Author(s):  
José R. Navarro ◽  
Jorge González ◽  
David Picó ◽  
Francisco Casacuberta ◽  
Joan M. de Val ◽  
...  
Keyword(s):  
Machine Translation ◽  
Translation System ◽  
Machine Translation System ◽  
Hybrid Machine ◽  
Hybrid Machine Translation
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