The level of explicitation of reference in the translation of medical texts from English into Persian: A case study on basic histology

Ahangar, Abbas Ali; Rahnemoon, Seyyedeh Nazanin

doi:10.1016/j.lingua.2019.06.005

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…The same case applied to Google Translate, which only apply reduction for 4,9% of the text. However, U-dictionary's usage of linguistic amplification is 5,6% while Google Translate's 4,8%, so it seems the findings in this study support Ahangar & Rahnemoon (2019), but only for Google Translate.…”

Section: Discussioncontrasting

confidence: 49%

“…Other researchers such as Ahangar & Rahnemoon (2019) stated that machine translations tend to omit or reduce target information, indicating a propensity to apply reduction translation technique and linguistic compression. However, this study revealed that U-dictionary actually tend to add target information.…”

Section: Discussionmentioning

confidence: 99%

“…As we know, there are currently eighteen translation techniques but machine translation cannot accommodate to all translation techniques. Machine translation tends to omit or reduce target information (Ahangar & Rahnemoon, 2019). Equivalence is a complex translation problem since the translator needs to choose better techniques to achieve the equivalence in source and target.…”

Section: (Borrowing)mentioning

confidence: 99%

“…This weakness occurred due to the difference on grammatical point of view. In addition to that, machine translation tends to reduce target information (Ahangar & Rahnemoon, 2019). It means that machine translation tends to use linguistic compression and reduction translation techniques in translating.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?

Sipayung

Sianturi

Arta

et al. 2021

Elsya

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Better translation produced by computation linguistics should be evaluated through linguistics theory. This research aims to describe translation techniques between Google Translate and U-Dictionary. The study used a qualitative research method with a descriptive design. This design was used to describe the occurrences of translation techniques in both translation machine, with the researchers serving as an instrument to compare translation techniques which is produced on machine. The data are from expository text entitled “Importance of Good Manners in Every Day Life”. The total data are 122 words/phrases which are pairs of translations, English as source language and Indonesia as target language. The result shows that Google Translate apply five of Molina & Albir’s (2002) eighteen translation techniques, while U-dictionary apply seven techniques. Google Translate dominantly apply literal translation techniques (86,8%) followed by reduction translation techniques (4,9%). U-dictionary also dominantly apply literal translation techniques (75,4%), but follows with the variation translation techniques (13,1%). This study showed that both machines produced different target texts for the same source language due to different applications of techniques, with U-dictionary proven to apply more variety of translation techniques than Google Translate. The researcher hopes this study can be used as an evaluation for improving the performance of machine translations.

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Section: Discussioncontrasting

confidence: 49%

Section: Discussionmentioning

confidence: 99%

Section: (Borrowing)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?

Sipayung

Sianturi

Arta

et al. 2021

Elsya

View full text Add to dashboard Cite

show abstract

“…In addition, she termed the shifts optional and pointed out they are conditioned by the discursive features of the genre. On the other hand, Ahangar and Rahnemoon (2019) sought to determine the level of explicitation of reference in the translation of medical texts from English into Persian. They concluded that the most frequently used tie in English was the and the most frequently employed tie in Persian was the personal endings as a whole.…”

Section: Introductionmentioning

confidence: 99%

Referential Cohesion in the Translation of English-swahili Healthcare Texts

Orang’i

2022

IJCLTS

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Referential cohesion in African languages is a largely neglected area of study. Given the foregoing, this study, theoretically anchored on Descriptive Translation Studies and Halliday and Hasan’s (1976) seminal model, explores referential cohesion in the translation of English-Swahili healthcare texts with an endeavour to describe its use and establish if there is variation when texts are translated. Cohesion in translation is fundamental since it is the basis of a network of relations that allows readers to interpret a text. The results show that personals and demonstratives are the most widely used references in both the source and target texts. Further, anaphoric reference is the most prevalent. In addition, it emerged that the translated Swahili text uses more reference items than their English counterparts. This is partly due to explicitation and the nature of the Swahili verb. This study brings to the fore the role of translators in making texts cohesive notwithstanding the reference items used in the source texts.

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The role of registerial expertise in texture re-creation in medical translation

Yue,

Fang

2024

LangCT

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This article investigates how an important aspect of translators’ registerial expertise — domain-specific (medical) training and experience — may have consequences for the texture of a specialised translation, i.e. the translations of the Chinese medicine classic Huang Di Nei Jing. Through a fine-grained analysis of cohesion and coherence, this study demonstrates that a translator with a higher level of domain-specific expertise tends to produce a translation (i) that is more lexically explicit and cohesively harmonious, (ii) with a richer semantic variety, more extended cohesive chains and cross-chain interactions and (iii) having a higher degree of definitiveness. The findings suggest that a translator’s domain-specific expertise could have measurable consequences for the texture of the target text.

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The level of explicitation of reference in the translation of medical texts from English into Persian: A case study on basic histology

Cited by 5 publications

References 21 publications

Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?

Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?

Referential Cohesion in the Translation of English-swahili Healthcare Texts

The role of registerial expertise in texture re-creation in medical translation

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