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.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

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 Word-Order Issues in English-to-Urdu Statistical Machine Translation

2011 ◽  
Vol 95 (1) ◽  
pp. 87-106 ◽  
Author(s):  
Bushra Jawaid ◽  
Daniel Zeman
Keyword(s):  
Machine Translation ◽  
Word Order ◽  
Statistical Machine Translation ◽  
Parse Tree ◽  
Hard Problem ◽  
Long Distance ◽  
Translation Process ◽  
English Sentence ◽  
European Languages

Word-Order Issues in English-to-Urdu Statistical Machine Translation We investigate phrase-based statistical machine translation between English and Urdu, two Indo-European languages that differ significantly in their word-order preferences. Reordering of words and phrases is thus a necessary part of the translation process. While local reordering is modeled nicely by phrase-based systems, long-distance reordering is known to be a hard problem. We perform experiments using the Moses SMT system and discuss reordering models available in Moses. We then present our novel, Urdu-aware, yet generalizable approach based on reordering phrases in syntactic parse tree of the source English sentence. Our technique significantly improves quality of English-Urdu translation with Moses, both in terms of BLEU score and of subjective human judgments.

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 Insertion-based Decoding with Automatically Inferred Generation Order

2019 ◽  
Vol 7 ◽  
pp. 661-676 ◽  
Author(s):  
Jiatao Gu ◽  
Qi Liu ◽  
Kyunghyun Cho
Keyword(s):  
Machine Translation ◽  
Real World ◽  
Word Order ◽  
Code Generation ◽  
State Of The Art ◽  
Generation Model ◽  
Beam Search ◽  
Input Information ◽  
Sequence Generation ◽  
Image Caption

Conventional neural autoregressive decoding commonly assumes a fixed left-to-right generation order, which may be sub-optimal. In this work, we propose a novel decoding algorithm— InDIGO—which supports flexible sequence generation in arbitrary orders through insertion operations. We extend Transformer, a state-of-the-art sequence generation model, to efficiently implement the proposed approach, enabling it to be trained with either a pre-defined generation order or adaptive orders obtained from beam-search. Experiments on four real-world tasks, including word order recovery, machine translation, image caption, and code generation, demonstrate that our algorithm can generate sequences following arbitrary orders, while achieving competitive or even better performance compared with the conventional left-to-right generation. The generated sequences show that InDIGO adopts adaptive generation orders based on input information.

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 Blitz Latin Revisited

2015 ◽  
Vol 16 (32) ◽  
pp. 43-49
Author(s):  
John F. White
Keyword(s):  
Machine Translation ◽  
Word Order ◽  
Latin Language ◽  
Key Issues

SummaryDevelopment of the machine translator Blitz Latin between the years 2002 and 2015 is discussed. Key issues remain the ambiguity in meaning of Latin stems and inflections, and the word order of the Latin language. Attempts to improve machine translation of Latin are described by the programmer.

A Syntactic Reordering Model of Chinese Special Sentences for Patent Machine Translation

2013 ◽  
Vol 411-414 ◽  
pp. 1923-1929
Author(s):  
Ren Fen Hu ◽  
Yun Zhu ◽  
Yao Hong Jin ◽  
Jia Yong Chen
Keyword(s):  
Machine Translation ◽  
Statistical Methods ◽  
Word Order ◽  
English Word ◽  
Long Distance ◽  
Rule Based

This paper presents a rule-based model to deal with the long distance reordering of Chinese special sentences. In this model, we firstly identify special prepositions and their syntax levels. After that, sentences are parsed and transformed to be much closer to English word order with reordering rules. We evaluate our method within a patent MT system, which shows a great advantage over reordering with statistical methods. With the presented reordering model, the performance of patent machine translation of Chinese special sentences is effectually improved.

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