Errors Found on the English Translation Sentences of the Third-Year Students of English at Ikip Budi Utomo

Fitriani, Enis

doi:10.33503/journey.v3i1.706

Cited by 7 publications

(9 citation statements)

References 1 publication

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“…Table 2 shows the frequency of errors found in the students' writing according to the taxonomy As can be seen from the table, errors from the Omission category top the list of errors committed by the students (55.0%) with 19.0% found in the Content and 36.0% in the Grammatical sub-categories. Although the findings are not consistent with several previous studies that reported Misformation as most prevalent among the error categories (Fitriani, 2020;Ozkayran and Yilmaz, 2020;Yakub and Hossain, 2018;Novita, 2014), the results indicate that content and grammatical omissions are the most problematic areas that should be accentuated by the educators when drawing any intervention program for these graduating students. Table 3 shows some examples of errors found in the students' writing scripts.…”

Section: Common Types Of Morphological Errors Made By Graduating Esl Students In Their Writingcontrasting

confidence: 99%

“…Only 11.3% of errors consisting of errors in Archi-forms (4.4%), Regularization (4.1%) and Alternation (2.8%) are evident throughout the writing scripts analyzed. Studies have claimed that errors in misformation are largely caused by students' lack of vocabulary and grammar development (Suhono, 2016;Fitriani, 2020). This is in line with the errors shown in Table 5 in the Misformation category in this study.…”

Section: Grammaticalsupporting

confidence: 90%

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Analysing Morphological Errors in ESL Graduating Students’ Writing based on Surface Structure Taxonomy

Ariffin

Darus

Halim

et al. 2021

IJMAL

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Empirical studies have examined errors committed by ESL learners at different levels of study in terms of its inconsistency with the standard form of English grammar rules in general. This study, on the other hand, looked into the errors in the English morphological system of a language that deals with the minimal units of linguistic form and meaning. By understanding the errors from the morphological perspective, instructors may also be able to figure out the specific causal factors that can help devise their methods and approach to teaching. As the English language performance of the graduates in this country has been publicly lamented by employers, this study focused on the writing products of graduating students to see the common errors made and to understand the interfering factors so that any intervention initiatives to improve their English language proficiency can be objectively proposed. The data were procured from a mock writing exit test of 60 graduating students from a public university. The Theory Taxonomy of Dulay or Surface Structure Taxonomy framework was used for data analysis. The data were treated quantitatively in terms of frequency counts of errors occurred in each category. The findings indicate that the errors were prevalent in the Omission category, followed by Addition, Misformation and Misordering. This has significant pedagogical implications on developing intervention or remedial programmes for graduating students.

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Section: Common Types Of Morphological Errors Made By Graduating Esl Students In Their Writingcontrasting

confidence: 99%

Section: Grammaticalsupporting

confidence: 90%

Analysing Morphological Errors in ESL Graduating Students’ Writing based on Surface Structure Taxonomy

Ariffin

Darus

Halim

et al. 2021

IJMAL

View full text Add to dashboard Cite

show abstract

“…Najjar et al explored the use of hyperbole in the Qur'an and its English translation, and they studied the morphological transformation of hyperbolic patterns [ 1 ]. Fitriani studied the syntactic and lexical aspects of the description and classification of grammatical errors found in English-translated sentences [ 2 ]. Yuval and Avishai described an optimization model based on dynamic programming and a public transport simulation that validates the benefits of such a model [ 3 ].…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, its complexity is mainly reflected in the following aspects. (1) e hierarchy of the architecture; (2) e complexity of information processing; (3) e uncertainty of system input and output information; (4) e intelligence of system behavior.…”

Section: Simulation Model Eigen Trust Is a Global Trust-based Model P...mentioning

confidence: 99%

[Retracted] A Novel English Translation Model in Complex Environments Using Two‐Stream Convolutional Neural Networks

Zhang

2022

Journal of Environmental and Public Health

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Although translation is an essential component of learning English, it does not receive the attention it merits in the modern English classroom. Teachers and students primarily emphasize listening, reading, and writing while neglecting the development of translation skills. The English test in China now reflects the fact that there are now very specific requirements for students’ translation skills. As a result, we should emphasize developing students’ translation skills when teaching them English. The following experimental data can be obtained following the study and experiment on the English translation simulation model based on the two-stream convolutional neural network: English vocabulary and grammar have passing and excellent rates of 90 and 57 percent, respectively, while reading has passing and excellent rates of 69 and 8 percent, respectively. The ability of students to translate into English has significantly improved after using the English translation simulation model based on the two-stream convolutional neural network.

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“…Fitriani's research tried to classify and describe grammatical faults in English-translated sentences in terms of syntax and morphology. e surface strategy taxonomy is used to guide the analytical approach [5]. Susini found that implicit meaning is one of the linguistic phenomena that need to be overcome in 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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Errors Found on the English Translation Sentences of the Third-Year Students of English at Ikip Budi Utomo

Cited by 7 publications

References 1 publication

Analysing Morphological Errors in ESL Graduating Students’ Writing based on Surface Structure Taxonomy

Analysing Morphological Errors in ESL Graduating Students’ Writing based on Surface Structure Taxonomy

[Retracted] A Novel English Translation Model in Complex Environments Using Two‐Stream Convolutional Neural Networks

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

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