scholarly journals OCR and Machine Translation

2021 ◽  
Author(s):  
Andrew Akhlaghi
Keyword(s):  
Machine Translation ◽  
Automated Translation

This lesson covers how to convert images of text into text files and translate those text files. The lesson will also cover how to organize and edit images to make the conversion and translation of whole folders of text files easier and more accurate. The lesson concludes with a discussion of the shortcomings of automated translation and how to overcome them.

scholarly journals Online Machine Translation: The Students’ Perspective during Quarantine Period Learning Activity

2020 ◽  
Vol 2 (1) ◽  
pp. 30-41
Author(s):  
Ratih Laily Nurjanah ◽  
Deswandito Dwi Saptanto
Keyword(s):  
Machine Translation ◽  
English Literature ◽  
Learning Activities ◽  
Learning Activity ◽  
Internet Connection ◽  
Digital Era ◽  
Automated Translation ◽  
Translation Study ◽  
Research Questions

The role of internet nowadays leads to the increase of online translation usage. The online translation offers various kinds of machine translation besides the-popular- Google Translate. The purpose of this research is to determine the students’ perspective on online machine translation they can easily found on internet related to the learning process on translation study during the quarantine period.. The research questions are; 1) What are the good sides of using online machine translation? 2)What are the shortages of using online machine translation? 3)How does online machine translation help students during learning activities on quarantine period? Online machine translation is automated translation or “translation carried out by a computer” with the internet connection. The subjects of this study were 6th semester students at English Literature Department of Universitas Ngudi Waluyo who had taken subjects related to translation. The research was conducted by delivering survey with google form to gather students’ perspectives. From the results, it is concluded that students were familiar with Google Translation as the online machine translation. Students stated that online machine translation often gives ambiguous translation. The use of online machine translation is helpful especially in terms of saving time. In conclusion, to keep up with the development of digital era, students need to be introduced to various online machine translation to help them work faster and keep improving their translation skill to back up the defects of online machines translation.

scholarly journals Youtube's Automated Translation and Google Translate: Possible Problems and Solutions

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Alvin Taufik
Keyword(s):  
Machine Translation ◽  
Translation Studies ◽  
Automated Translation ◽  
Problems And Solutions

<p>Translation technology has now been acknowledged as a subdomain of translation studies (Christensen, Flanagan, and Schjoldager, 2017). The researches on translation technology cover a lot of aspects of translation as well as the technology that is used to aid the process. This research is focused on the latter, especially on the results of the Human-aided Machine Translation (HAMT). The HAMT which becomes the focus of this research are Youtube’s Automated Translation and Google Translate (AT and GT respectively). The aim of this research is to evaluate the HAMTs, identify the problems, and propose a possible solution for the problems. This research is using a realism approach because it uses the translator students’ perspective of the HAMT, and the use of corpora. Questionnaires and experiments will be given and performed to assess and to formulate the solutions. <strong></strong></p><strong>Keywords: </strong> HAMT, GT, AT, realism, corpora

scholarly journals Machine Translation in the Hands of Trainee Translators – an Empirical Study

2017 ◽  
Vol 49 (1) ◽  
pp. 195-212 ◽  
Author(s):  
Joanna Sycz-Opoń ◽  
Ksenia Gałuskina
Keyword(s):  
Empirical Study ◽  
Machine Translation ◽  
Text Analysis ◽  
Special Kind ◽  
Knowledge And Skills ◽  
Automated Translation ◽  
French And English ◽  
Research Questions ◽  
Translation Machine ◽  
Effective Use

Abstract Automated translation (machine translation, MT) is systematically gaining popularity among professional translators, who claim that editing MT output requires less time and effort than translating from scratch. MT technology is also offered in leading translator’s workstations, e.g., SDL Trados Studio, memoQ, Déjà Vu and Wordfast. Therefore, the dilemma arises: should MT be introduced into formal translation training? In order to answer this question, first, it is necessary to understand how trainee translators actually use MT. This study is an attempt to obtain this knowledge. The methodology applied in this investigation is text analysis. During the experiment sessions the students were asked to translate a legal text using MT tools, which in practice meant the post-editing of the MT raw output. The post-edited versions of the text underwent analysis in order to answer the following research questions: - What are the most typical errors contained in both French and English MT output? - How critical are the students towards the text generated by MT? - How perceptive are the students during the post-editing task? - Are they able to detect and correct errors using their knowledge and skills? The results of this study suggest that the post-editing of the MT raw output is as demanding for translation students as traditional translation, however, it requires a different set of skills, such as critical thinking and perceptiveness. Therefore, a special kind of training related to the effective use of MT technology should be implemented during translation classes.

scholarly journals AN OVERVIEW OF AUTOMATED TRANSLATION AND ITS LINGUISTIC PROBLEMS

2021 ◽  
pp. 139-149
Keyword(s):  
Natural Language ◽  
Machine Translation ◽  
Computational Linguistics ◽  
Language Processing ◽  
Information Exchange ◽  
Rapid Development ◽  
Language Translation ◽  
Target Language ◽  
Technical Documentation ◽  
Automated Translation

Language, as the information carrier, has become the most significant means for humans to communicate. However, it has been considered as the barrier of communications between people from different countries. The problem of converting a language quickly and efficiently has become a problem of common concern for humanity. In fact, the demand for language translation has greatly increased in recent times due to effect of cross-regional communication and the need for information exchange. Most material needs to be translated, including scientific and technical documentation, instruction manuals, legal documents, textbooks, publicity leaflets, newspaper reports, etc. The issue is challenging and difficult but mostly it is tedious and repetitive and requires consistency and accuracy. It is becoming difficult for professional translators to meet the increasing demands of translation. In such a situation, the machine translation can be used as a substitute. Machine Translation is the process of converting a natural source language into another natural target language by computer. It is a branch of natural language processing and it has a close relationship with computational linguistics and natural language understanding. With the rapid development of the Internet and the integration of the world economy, how to overcome the barrier of language has become a common problem of the international community. This paper offers an overview of Machine Translation (MT) including the history of MT, linguistic problems of MT, the problem of multiple meanings in MT, syntactic transformations in MT, translation of phraseological combinations in MT systems.

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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