Overcoming Implicit Meaning in Indonesian-English Translation

Susini, Made

doi:10.22225/jr.6.1.1646.63-68

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Implicit meaning is one of the linguistic phenomena that need to be overcome in translation. e Susini study aimed to investigate in what structure the implicit meaning is realized in Indonesian and how the implicit meaning is handled when it is translated into English [4]. Although the structures of the source and target languages differed in the translations studied, the meaning of the source language was successfully conveyed in English.…”

Section: Related Workmentioning

confidence: 99%

English Language Intelligent Translation System Based on 3D Visualization Technology

Song

2022

Mobile Information Systems

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In the process of imparting knowledge, faculty should give more emphasis to the acquisition of students’ direct experience. Vivid three-dimensional visualization resources should be used for classroom teaching to help students better understand and grasp the visualization of knowledge. Based on this, this paper aims to study the English language intelligent translation system based on 3D visualization technology. In recent years, 3D application technology is transforming into multiuser applications supporting network environments. This transformation also promotes the application and development of 3D GIS technology. Transformer is a universal and efficient feature extractor. This paper proposes a design scheme for constructing 3D interactive visualization resources in teaching scenes. It is designed for specific disciplines and produces visual resources. Then, it analyzes and evaluates the actual effect of the application in teaching, to provide ideas for English translation education experts and teaching staff. It also builds a translation system based on B/S architecture, connects the server model and the client, and realizes the visualization and interaction of translation functions. In the experiments, 5% and 20% of the parallel corpus with a total of 12 M sentence pairs were used for pretraining of the translation model. Then, the number of monolingual corpus is gradually increased to train the model. Experiments show that when using the same amount of monolingual corpus, the translation model can achieve a more obvious effect on the pretrained model.

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Section: Related Workmentioning

confidence: 99%

English Language Intelligent Translation System Based on 3D Visualization Technology

Song

2022

Mobile Information Systems

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“…Susini found that implicit meaning is one of the linguistic phenomena that need to be overcome in translation. His research aims to investigate the structure in which implicit meaning is realized in English [6]. Although scholars agree that English translation is very important, there is a lack of research on how to improve the effect of college English translation.…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Network Based on Deep Learning in College English Translation Teaching Management

2022

Mobile Information Systems

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With the increased demand for English translators in recent years, more scholars and researchers have begun to focus on college English translation education. The demand for computers to perform automatic natural language translation is at an all-time high, presenting another potential for machine translation (MT) research. Although neural machine translation (NMT) technology has advanced significantly, it still has several flaws. The reply NN-based MT technology is not optimal in the field of long sentence translation, and there are situations such as missing translation and overtranslation. So this paper proposes college English translation based on a convolutional neural network (CNN). This paper proposes that CNNs have stronger feature extraction and information processing capabilities than other deep NNs. It successfully solves the situation of missing translation and overtranslation, lowering the quality of college English translation instruction. The experimental results of this research reveal that when the length of an English sentence grows longer, the translation accuracy based on the reply NN drops to 47.9%, while the translation accuracy based on the CNN is 89.7%. It can be seen that college English translation based on CNN can be integrated into English translation teaching as an innovation of traditional teaching methods, so as to create a learning environment for students and enhance learning effects. MT based on CNN has greatly improved students’ performance, which is conducive to improving the quality of college English translation teaching.

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“…In line with Catford (1965), a translation is a process of substituting equivalent content from one language (Source Language) into another language (Target Language). Translating is not an action that is done simply by translating the text literally, but the translation contains essential information that the author of source text wants to convey so "the target text must communicate the same meaning as the source text communicates" (Susini, 2020). The exact meanings in this term refer to not only what are provided explicitly in the text but also what lies implicitly through a context (Liddicoat, 2015).…”

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confidence: 99%

Types of Translation Errors from Indonesia Language into English in Pharmacy Journal Articles

Anindya¹,

Yonatri

2022

RETORIKA: Jurnal Ilmu Bahasa

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The study analyzed the types of errors based on ATA Error Categories version 2021 on the three submitted journal articles translated by lecturers of Clinical Pharmacy and Pharmacy Community of Akademi Farmasi Surabaya. It aimed to find the types of errors often made by pharmacy lecturers in translating the articles from the Indonesian language into English so that the results of this study can help them identify and avoid errors in translation. The study used the descriptive qualitative, using the content analysis method. The objects of this study were three journal articles in the Indonesian language (ST) and English (TT). The data focused on the translation errors based on ATA Error Categories in the words or phrases level. The results showed there were 14 types of errors found, namely: literalness (31.05%), usage (22.22%), punctuation (7.84%), terminology (7.52%), verb form (6.86%), grammar (6.54%), capitalization (3.59%), parts of speech (3.59%), syntax (3.27%), style (2.94%), register (2.29%), omission (1.31%), cohesion (0.65%), and ambiguity (0.33%). The study recommended that translators have sufficient knowledge about the target language mechanics, produce clear renditions of the source text, and maintain the writing quality of the target text.

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Overcoming Implicit Meaning in Indonesian-English Translation

Cited by 4 publications

References 4 publications

English Language Intelligent Translation System Based on 3D Visualization Technology

English Language Intelligent Translation System Based on 3D Visualization Technology

Application of Convolutional Neural Network Based on Deep Learning in College English Translation Teaching Management

Types of Translation Errors from Indonesia Language into English in Pharmacy Journal Articles

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