scholarly journals Machine Translation Quality Assessment of <i>Selected Works of Xiaoping Deng</i> Supported by Digital Humanistic Method

2021 ◽  
Vol 7 (2) ◽  
pp. 59
Author(s):  
Qing Wang ◽  
Xiao Ma
Keyword(s):  
Quality Assessment ◽  
Machine Translation ◽  
Translation Quality

Development of a method for assessing the quality of machine translation based on ensemble methods in machine learning

2021 ◽  
Author(s):  
A.V. Kozina ◽  
Yu.S. Belov
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Quality Assessment ◽  
Machine Translation ◽  
Translation System ◽  
Human Judgment ◽  
Translation Quality ◽  
Machine Translation System ◽  
Translation Systems

Automatically assessing the quality of machine translation is an important yet challenging task for machine translation research. Translation quality assessment is understood as predicting translation quality without reference to the source text. Translation quality depends on the specific machine translation system and often requires post-editing. Manual editing is a long and expensive process. Since the need to quickly determine the quality of translation increases, its automation is required. In this paper, we propose a quality assessment method based on ensemble supervised machine learning methods. The bilingual corpus WMT 2019 for the EnglishRussian language pair was used as data. The text data volume is 17089 sentences, 85% of the data was used for training, and 15% for testing the model. Linguistic functions extracted from the text in the source and target languages were used as features for training the system, since it is these characteristics that can most accurately characterize the translation in terms of quality. The following tools were used for feature extraction: a free language modeling tool based on SRILM and a Stanford POS Tagger parts of speech tagger. Before training the system, the text was preprocessed. The model was trained using three regression methods: Bagging, Extra Tree, and Random Forest. The algorithms were implemented in the Python programming language using the Scikit learn library. The parameters of the random forest method have been optimized using a grid search. The performance of the model was assessed by the mean absolute error MAE and the root mean square error RMSE, as well as by the Pearsоn coefficient, which determines the correlation with human judgment. Testing was carried out using three machine translation systems: Google and Bing neural systems, Mouses statistical machine translation systems based on phrases and based on syntax. Based on the results of the work, the method of additional trees showed itself best. In addition, for all categories of indicators under consideration, the best results are achieved using the Google machine translation system. The developed method showed good results close to human judgment. The system can be used for further research in the task of assessing the quality of translation.

scholarly journals Book Review: Translation Quality Assessment: From Principles to Practice

2020 ◽  
pp. 110-115
Author(s):  
Rocío Caro Quintana
Keyword(s):  
Quality Assessment ◽  
Machine Translation ◽  
Computer Software ◽  
Digital Content ◽  
Translation Quality ◽  
The World

With the growth of digital content and the consequences of globalization, more content is published every day and it needs to be translated in order to make it accessible to people all over the world. This process is very simple and straightforward thanks to the implementation of Machine Translation (MT), which is the process of translating texts automatically with computer software in a few seconds. Nevertheless, the quality of texts has to be checked to make them comprehensible, since the quality from MT is still far from perfect. Translation Quality Assessment: From Principles to Practice, edited by Joss Moorkens, Sheila Castilho, Federico Gaspari and Stephen Doherty (2018), deals with the different ways (automatic and manual) these translations can be evaluated. The volume covers how the field has changed throughout the decades (from 1978 until 2018), the different methods it can be applied, and some considerations for future Translation Quality Assessment applications.

scholarly journals Acceptability of machine-translated content: A multi-language evaluation by translators and end-users

2018 ◽  
Vol 16 ◽  
Author(s):  
Sheila Castilho ◽  
Sharon O'Brien
Keyword(s):  
Quality Assessment ◽  
Machine Translation ◽  
End Users ◽  
Quality Review ◽  
Translation Quality ◽  
Industry Partner ◽  
Simplified Chinese ◽  
Instructional Content ◽  
Giving Feedback

As machine translation (MT) continues to be used increasingly in the translation industry, there is a corresponding increase in the need to understand MT quality and, in particular, its impact on end-users. To date, little work has been carried out to investigate the acceptability of MT output among end-users and, ultimately, how acceptable they find it. This article reports on research conducted to address that gap. End-users of instructional content machine-translated from English into German, Simplified Chinese and Japanese were engaged in a usability experiment. Part of this experiment involved giving feedback on the acceptability of raw machine-translated content and lightly post-edited (PE) versions of the same content. In addition, a quality review was carried out in collaboration with an industry partner and experienced translation quality reviewers. The translation quality-assessment (TQA) results from translators reflect the usability and satisfaction results by end-users insofar as the implementation of light PE both increased the usability and acceptability of the PE instructions and led to satisfaction being reported. Nonetheless, the raw MT content also received good scores, especially for terminology, country standards and spelling.

scholarly journals Context-Aware Neural Machine Translation for Korean Honorific Expressions

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1589
Author(s):  
Yongkeun Hwang ◽  
Yanghoon Kim ◽  
Kyomin Jung
Keyword(s):  
Machine Translation ◽  
Deep Neural Networks ◽  
Contextual Information ◽  
Context Aware ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Sentence Level ◽  
Proposed Model ◽  
The Given ◽  
The Relationship

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.

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