Translation accuracy of a technical credentialing examination

2002 ◽  
Vol 12 (1/2/3/4) ◽  
pp. 167
Author(s):  
Mark D. Reckase ◽  
Charles Kunce
Keyword(s):  
Credentialing Examination ◽  
Translation Accuracy

The Translation Problem Types in Translating Indonesia Text to English (A Case Study of Translation Subject of Fifth Semester English Students –UPY)

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Sujatmiko Sujatmiko
Keyword(s):  
Qualitative Method ◽  
English Department ◽  
Problem Types ◽  
Translation Technique ◽  
Data Source ◽  
Word Translation ◽  
Translation Accuracy ◽  
English Students ◽  
Successful Transfer

This research is entitled “The Translation Problem Types in Translating Indonesia textinto English (A Case Study of Translation Subject of Fifth Semester English Department –UPY) . It is about how Indonesia text is translated into English by English students, toidentify the translation problems, and to identify the problematics of translation technique.This research uses qualitative method to analyze the data. Techniques of analyzing datain this research consist of 3 components, they are (1) reducing the data, (2) explaining thedata, and (3) taking a conclusion. Reducing data is a process of selecting, focusing,simplifying and abstracting the data. Explaining the data is a process of organizinginformation and arranging the complete narration. Taking a conclusion is a process ofdrawing conclusion from the data. The data source of this research are Indonesia text andstudent’s translation.After conducting the research, the research find the data that all respondents havesimilar translation problem types in translating Indonesia text into English. The problems arediction, tenses, no equivalence translation; others have problems of adverb, article, andrelative clause. None of respondents apply other translation technique. They only apply wordper word translation technique. The accuracy of transfer level is adequate level. Only onerespondent have almost completely successful transfer level. Other respondents haveadequate accuracy transfer level. By applying the untrue translation technique has an impactto translation accuracy transfer level.This research is expected to open wide opportunities and challenges to academicians,especially those in translation linguistics sphere to deepen their research and study, especiallyin translating Indonesia text to English in order to be a new contribution to the translationfields.

Analysis Accuracy of Similar Word Based Clustering (EWSB) Algorithm on Machine Translator Bahasa Indonesia-Minang

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Herry Sujaini
Keyword(s):  
Machine Translation ◽  
Clustering Algorithm ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Word Similarity ◽  
Similar Word ◽  
Word Clustering ◽  
Translation Accuracy ◽  
Bahasa Indonesia

Extended Word Similarity Based (EWSB) Clustering is a word clustering algorithm based on the value of words similarity obtained from the computation of a corpus. One of the benefits of clustering with this algorithm is to improve the translation of a statistical machine translation. Previous research proved that EWSB algorithm could improve the Indonesian-English translator, where the algorithm was applied to Indonesian language as target language.This paper discusses the results of a research using EWSB algorithm on a Indonesian to Minang statistical machine translator, where the algorithm is applied to Minang language as the target language. The research obtained resulted that the EWSB algorithm is quite effective when used in Minang language as the target language. The results of this study indicate that EWSB algorithm can improve the translation accuracy by 6.36%.

scholarly journals Random errors in protein synthesis activate an age-dependent program of muscle atrophy in mice

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
James Moore ◽  
Rashid Akbergenov ◽  
Martina Nigri ◽  
Patricia Isnard-Petit ◽  
Amandine Grimm ◽  
...  
Keyword(s):  
Protein Synthesis ◽  
Muscle Atrophy ◽  
Strength Analysis ◽  
Dependent Manner ◽  
Random Errors ◽  
Age Related ◽  
Related Phenotype ◽  
Age Dependent ◽  
Translation Accuracy ◽  
Transcriptomic Changes

AbstractRandom errors in protein synthesis are prevalent and ubiquitous, yet their effect on organismal health has remained enigmatic for over five decades. Here, we studied whether mice carrying the ribosomal ambiguity (ram) mutation Rps2-A226Y, recently shown to increase the inborn error rate of mammalian translation, if at all viable, present any specific, possibly aging-related, phenotype. We introduced Rps2-A226Y using a Cre/loxP strategy. Resulting transgenic mice were mosaic and showed a muscle-related phenotype with reduced grip strength. Analysis of gene expression in skeletal muscle using RNA-Seq revealed transcriptomic changes occurring in an age-dependent manner, involving an interplay of PGC1α, FOXO3, mTOR, and glucocorticoids as key signaling pathways, and finally resulting in activation of a muscle atrophy program. Our results highlight the relevance of translation accuracy, and show how disturbances thereof may contribute to age-related pathologies.

Improved neural machine translation for low-resource English–Assamese pair

2021 ◽  
pp. 1-12
Author(s):  
Sahinur Rahman Laskar ◽  
Abdullah Faiz Ur Rahman Khilji ◽  
Partha Pakray ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Machine Translation ◽  
Data Augmentation ◽  
Language Translation ◽  
Linguistically Diverse ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Parallel Data ◽  
The World ◽  
Translation Accuracy ◽  
Vocabulary Problems

Language translation is essential to bring the world closer and plays a significant part in building a community among people of different linguistic backgrounds. Machine translation dramatically helps in removing the language barrier and allows easier communication among linguistically diverse communities. Due to the unavailability of resources, major languages of the world are accounted as low-resource languages. This leads to a challenging task of automating translation among various such languages to benefit indigenous speakers. This article investigates neural machine translation for the English–Assamese resource-poor language pair by tackling insufficient data and out-of-vocabulary problems. We have also proposed an approach of data augmentation-based NMT, which exploits synthetic parallel data and shows significantly improved translation accuracy for English-to-Assamese and Assamese-to-English translation and obtained state-of-the-art results.

scholarly journals Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 7839-7846
Author(s):  
Junliang Guo ◽  
Xu Tan ◽  
Linli Xu ◽  
Tao Qin ◽  
Enhong Chen ◽  
...  
Keyword(s):  
Machine Translation ◽  
Fine Tuning ◽  
Inference Process ◽  
Neural Machine Translation ◽  
Training Strategy ◽  
Speed Up ◽  
Good Improvement ◽  
Tuning Process ◽  
Translation Accuracy ◽  
The Cost

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than 1 BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than 10 times) the inference process over AT baselines.

scholarly journals Slang Language in the Subtitle of The Social Network Movie

K ta Kita ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 167-172
Author(s):  
Kevin Sienatra
Keyword(s):  
Social Network ◽  
Descriptive Study ◽  
The Other ◽  
Daily Lives ◽  
Translation Quality ◽  
The Social ◽  
Qualitative Descriptive ◽  
Word Translation ◽  
Similarities And Differences ◽  
Translation Accuracy

Movies are everyday entertainment for people in their daily lives. There are a lot of foreign movies that are being played in Indonesian theatres. Unfortunately, there are many places where people watch the movie with the subtitles that are not created by the professional translators. The Social Network was translated by more than one translator. This research was conducted to analyze how accurate the translation is and what the similarities and differences between the translators are. This study is a qualitative descriptive study, which analyzes the slang word translation accuracy in the movie The Social Network using Newmark theories of translation quality assessment. The finding of the study showed that the translation from both of the translators is accurate enough and there is almost no inaccurate translation, also there are several slang words that are not included in the data of the previous study, but the writer found on subtitle the data of the other two translators.Keywords: Slang, Translation, Accuracy

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