Machine Translation without a source text

In the present study, we investigate the post-editese phenomenon, i.e., the unique features that set machine translated post-edited texts apart from human-translated texts. We use two literary texts, namely, the English children's novel by Lewis Carroll Alice’s Adventures in Wonderland (AW) and Paula Hawkins' popular book The Girl on the Train (TGOTT) translated from English into Brazilian-Portuguese to investigate whether the post-editese features can be found on the surface of the post-edited (PE) texts. In addition, we examine how the features found in the PE texts differ from the features encountered in the human-translated (HT) and machine translation (MT) versions of the same source text. Results revealed evidence for post-editese for TGOTT only with PE versions being more similar to the MT output than to the HT texts.

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English text to Indian Sign Language Machine Translation: A Rule Based Method

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j1084.08810s19 ◽

2019 ◽

Vol 8 (10S) ◽

pp. 460-467

Keyword(s):

Sign Language ◽

Machine Translation ◽

Hard Of Hearing ◽

Research Work ◽

Target Language ◽

English Text ◽

Source Text ◽

Intermediate Phases ◽

Translation Mechanism ◽

Indian Sign Language

The hearing challenged community all over world face difficulties to communicate with others. Machine translation has been one of the prominent technologies to facilitate a two way communication to the deaf and hard of hearing community all over the world. We have explored and formulated the fundamental rules of Indian Sign Language and implemented as a translation mechanism of English Text to Indian sign Language glosses. The structure of the source text is identified and transferred to the target language according to the formulated rules and sub rules. The intermediate phases of the transfer process is also mentioned in this research work.

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A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis

Computational Intelligence and Neuroscience ◽

10.1155/2021/3274326 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Qianyu Cao ◽

Hanmei Hao

Keyword(s):

Neural Network ◽

Big Data ◽

Data Analysis ◽

Machine Translation ◽

Big Data Analysis ◽

Translation System ◽

Source Text ◽

Chaotic Neural Network ◽

Passive Voice ◽

Machine Translation System

In this paper, the chaotic neural network model of big data analysis is used to conduct in-depth analysis and research on the English translation. Firstly, under the guidance of the translation strategy of text type theory, the translation generated by the machine translation system is edited after translation, and then professionals specializing in computer and translation are invited to confirm the translation. After that, the errors in the translations generated by the machine translation system are classified based on the Double Quantum Filter-Muttahida Quami Movement (DQF-MQM) error type classification framework. Due to the characteristics of the source text as an informative academic text, long and difficult sentences, passive voice, and terminology translation are the main causes of machine translation errors. In view of the rigorous logic of the source text and the fixed language steps, this research proposes corresponding post-translation editing strategies for each type of error. It is suggested that translators should maintain the logic of the source text by converting implicit connections into explicit connections, maintain the academic accuracy of the source text by adding subjects and adjusting the word order to deal with the passive voice, and deal with semitechnical terms by appropriately selecting word meanings in postediting. The errors of machine translation in computer science and technology text abstracts are systematically categorized, and the corresponding post-translation editing strategies are proposed to provide reference suggestions for translators in this field, to improve the quality of machine translation in this field.

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The death of the translator in machine translation

Target ◽

10.1075/target.23.1.06lee ◽

2011 ◽

Vol 23 (1) ◽

pp. 92-112 ◽

Cited By ~ 4

Author(s):

Tong King Lee

Keyword(s):

Machine Translation ◽

Human Agency ◽

Literary Text ◽

Target Language ◽

Source Text ◽

Avant Garde ◽

Bilingual Poetry ◽

New Meanings ◽

Physical Medium ◽

Death Of The Author

This paper explores the notion of the death of the Translator, inspired by Barthes’ formulation of the death of the Author. It argues that the death of the Translator is caused by a loss of human agency in translation and is therefore most clearly exemplified in machine translation. Based on an avant-garde bilingual poetry project by a Taiwanese poet, the paper demonstrates that machine translation can produce unexpected new meanings through unpredictable routes of semantic and syntactic divergences from the source text. The poet’s use of transparency as physical medium and of machine translation as mediator raises the following questions: does translation actually allow us to ‘read through’ a source text? If so, to what extent is such translation ‘transparent’? How should we even come to terms with the concept of ‘transparency’ with respect to the meaning of a literary text in translation? The paper argues that in the bilingual project in question, machine translation plays the crucial function of bringing the reader’s attention back to the target language by way of delaying/blocking comprehension, hence rendering the corporeality of the target language ‘transparent’.

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Translating Ideology: An Intergroup Mediation Perspective

Russian Journal of Linguistics ◽

10.22363/2312-9182-2019-23-2-383-398 ◽

2019 ◽

Vol 23 (2) ◽

pp. 383-398

Author(s):

Natalia Viktorovna Yarkina ◽

Liudmila Pavlovna Yarkina ◽

Ivan Alekseevich Pougachev

Keyword(s):

Machine Translation ◽

News Media ◽

Source Text ◽

Intergroup Threat ◽

Text Production ◽

Factors Influencing ◽

Translation Practice ◽

News Industry ◽

Intergroup Threat Theory ◽

Threat Theory

Ideology is an important component of text production and reception, and therefore of translation. In the paper, we address the translation of ideology through the prism of the intergroup threat theory. The resulting intergroup mediation perspective is a practical framework aimed at helping translators to evaluate whether an ideological recontextualisation may be desirable when dealing with divergence in ideological contexts between the author, the source text readership and the target text audience. The framework includes ideology shifts analysis in terms of the roles ideology can play in a text: it can constitute a part of the message (foreground) or belong to the background. Although applicable rather generally, the framework is derived from an example-based study of news translation. The study focuses on translation between French, English and German using examples form online versions of major European news media, such as the French Le Monde and Le Figaro , the German Zeit Online and Die Welt , and the British The Independent , The Telegraph and Guardian . The paper allows for a better understanding of ideology-related problems in translation, helps identify essential factors influencing translator’s choices and could be used as a guidance in translation practice. Also, considering the formal character of the framework, it could eventually serve as a basis for handling ideology-related issues in machine translation in the news industry.

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Is machine translation ready yet?

Target ◽

10.1075/target.22.1.02gar ◽

2010 ◽

Vol 22 (1) ◽

pp. 7-21 ◽

Cited By ~ 29

Author(s):

Ignacio Garcia

Keyword(s):

Machine Translation ◽

Source Text ◽

Default Option ◽

Translation Quality ◽

Test Criteria

The default option of the Google Translator Toolkit (GTT), released in June 2009, is to “pre-fill with machine translation” all segments for which a ‘no match’ has been returned by the memories, while the Settings window clearly advises that “[m]ost users should not modify this”. To confirm whether this approach indeed benefits translators and translation quality, we designed and performed tests whereby trainee translators used the GTT to translate passages from English into Chinese either entirely from the source text, or after seeding of empty segments by the Google Translate engine as recommended. The translations were timed, and their quality assessed by independent experienced markers following Australian NAATI test criteria. Our results show that, while time differences were not significant, the machine translation seeded passages were more favourably assessed by the markers in thirty three of fifty six cases. This indicates that, at least for certain tasks and language combinations—and against the received wisdom of translation professionals and translator trainers—translating by proofreading machine translation may be advantageous.

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Pre-Editing of Google Neural Machine Translation

Journal of English Language and Culture ◽

10.30813/jelc.v10i2.2137 ◽

2020 ◽

Vol 10 (2) ◽

Author(s):

Alvin Taufik

Keyword(s):

Machine Translation ◽

Focus Of Attention ◽

Source Text ◽

Neural Machine Translation ◽

Efficiency And Effectiveness ◽

The One ◽

Editing Process ◽

Oriented Research ◽

Language Pair ◽

New Machine

Even with the new Machine Translation (MT) platform available in Google today (Neural, as compared to the previous Statistical one in the previous years), the output is not always satisfactory. This is even more obvious in specific contexts and situations. Research has shown that the implementation of rules for the process prior to and the one that follows the input activities into an MT (often referred to as the pre-editing and post editing process) has proven to be fruitful (Gerlach, et. al., 2013; Shei, 2002). However, to the best knowledge of the researcher, no research on pre-editing rules on Indonesian input into MT has been conducted. This research is significant because it might increase efficiency and effectiveness of MT, especially for the language pair Indonesian-English. For that reason, this research intends to identify the pre-editing rules required to create a solid basis to translate Indonesian Source Text (ST) into English Target Text (TT). This research adopts the product-oriented research. The results show that in the pre-editing process, the length of the sentence, the conjunctions (subordinative and correlative), and the inappropriate ST words should be the focus of attention.

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Errors of Machine Translation of terminology in the patent text from English into Chinese

ASP Transactions on Computers ◽

10.52810/tc.2021.100022 ◽

2021 ◽

Vol 1 (1) ◽

pp. 12-17

Author(s):

Ying Cheng ◽

Shuyu Yue ◽

Jing Li ◽

Lin Deng ◽

Qi Quan

Keyword(s):

Machine Translation ◽

Common Noun ◽

Source Text ◽

Types Of Error

This paper summarizes eight types of error of terminology in the patent text in the output of Machine Translation from English into Chinese, including term being mistranslated as a verb, term being mistranslated as a common noun, term being redundantly translated, term being mistranslated as a homophone, term being mistranslated as a wrong term, term being mistranslated due to Chinese expression, term being mistranslated without initial, and term being mistranslated due to wrong acronym. These errors can be solved by the translator before Machine Translation and the translator can identify and correct these errors by pre-editing of the source text.

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