The Varibility of Reproduction: Emotive Units in a Literary Text (On the material of Ukrainian, Russian and Chinese) Variation of reflection of English-speaking emotional units in the translation of an artistic work (in Ukrainian, Russian, Chinese)

2019 ◽  
Vol 26 (27) ◽  
pp. 211-222
Author(s):  
Yana Fedorko ◽  
Tetiana Yablonskaya
Keyword(s):  
Key Words ◽  
Machine Translation ◽  
Political Discourse ◽  
Literary Text ◽  
Translation Quality ◽  
Automatic Translation ◽  
Advantages And Disadvantages ◽  
English Speaking ◽  
Translation Systems

The article is focused on peculiarities of English and Chinese political discourse translation into Ukrainian. The advantages and disadvantages of machine translation are described on the basis of linguistic analysis of online Google Translate and M-Translate systems. The reasons of errors in translation are identified and the need of post-correction to improve the quality of translation is wanted. Key words: political discourse, automatic translation, online machine translation systems, machine translation quality assessment.

scholarly journals Automatic evaluation of the quality of machine translation of a scientific text: the results of a five-year-long experiment

2021 ◽  
Vol 284 ◽  
pp. 08001
Author(s):  
Ilya Ulitkin ◽  
Irina Filippova ◽  
Natalia Ivanova ◽  
Alexey Poroykov
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Dramatic Improvement ◽  
Automatic Evaluation ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Automatic Translation ◽  
Translation Systems ◽  
Qualitative Changes

We report on various approaches to automatic evaluation of machine translation quality and describe three widely used methods. These methods, i.e. methods based on string matching and n-gram models, make it possible to compare the quality of machine translation to reference translation. We employ modern metrics for automatic evaluation of machine translation quality such as BLEU, F-measure, and TER to compare translations made by Google and PROMT neural machine translation systems with translations obtained 5 years ago, when statistical machine translation and rule-based machine translation algorithms were employed by Google and PROMT, respectively, as the main translation algorithms [6]. The evaluation of the translation quality of candidate texts generated by Google and PROMT with reference translation using an automatic translation evaluation program reveal significant qualitative changes as compared with the results obtained 5 years ago, which indicate a dramatic improvement in the work of the above-mentioned online translation systems. Ways to improve the quality of machine translation are discussed. It is shown that modern systems of automatic evaluation of translation quality allow errors made by machine translation systems to be identified and systematized, which will enable the improvement of the quality of translation by these systems in the future.

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 Machine Translation Quality of Khalil Gibran’s The Prophet

2017 ◽  
Author(s):  
AWEJ for Translation & Literary Studies ◽  
Zakaryia Mustafa Almahasees
Keyword(s):  
Machine Translation ◽  
Low Cost ◽  
Literary Texts ◽  
Future Research ◽  
Output Analysis ◽  
Translation Quality ◽  
Automatic Translation ◽  
Word Level ◽  
Full Analysis ◽  
Translation Systems

Machine translation (MT) systems are widely used throughout the world freely or at low cost. The spread of MT entails a thorough analysis of translation produced by such translation systems. The present study evaluates the capacity of two MT systems-Google Translate and Microsoft Bing translator- in translation from Arabic into English of Khalil Gibran’s literary masterpiece - The Prophet (2000). The question that arises in the study is could we trust MT in the translation of literary masterpieces across languages and particularly from Arabic to English? How close does MT output to human translation? To conduct that, the study is adopted Bilingual Evaluation Understudy (BLEU) of Papineni (2000). MT output analysis showed that MT is not accurate, intelligible and natural in translating literary texts due to the difficulty of literary texts, as they are full of metaphors and cultural specifications. Besides, there are some linguistic errors: lexical, syntactic and misinformation. The study also found that both systems provided similar translation for the same input due to either the use of similar MT approach or learning from previous translated texts. Moreover, both systems in some instances, achieve good results at the word level, but bad results at collocation units. The study also showed that automatic translation is insufficient for providing a full analysis of MT output because all automatic metrics are misleading due to dependence on text similarity to a reference human translation. For future research, the study recommended conducting a correlative study that combines manual and automatic evaluation methods to ensure best analysis of MT output. Machine Translation (MT) is still far from reaching fully automatic translation of a quality obtained by human translators.

scholarly journals The Role of Machine Translation Quality Estimation in the Post-Editing Workflow

Informatics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 61
Author(s):  
Hannah Béchara ◽  
Constantin Orăsan ◽  
Carla Parra Escartín ◽  
Marcos Zampieri ◽  
William Lowe
Keyword(s):  
Cognitive Load ◽  
Machine Translation ◽  
Impact Study ◽  
Quality Estimation ◽  
Editing Efficiency ◽  
Translation Quality ◽  
Automatic Translation ◽  
New Challenges

As Machine Translation (MT) becomes increasingly ubiquitous, so does its use in professional translation workflows. However, its proliferation in the translation industry has brought about new challenges in the field of Post-Editing (PE). We are now faced with a need to find effective tools to assess the quality of MT systems to avoid underpayments and mistrust by professional translators. In this scenario, one promising field of study is MT Quality Estimation (MTQE), as this aims to determine the quality of an automatic translation and, indirectly, its degree of post-editing difficulty. However, its impact on the translation workflows and the translators’ cognitive load is still to be fully explored. We report on the results of an impact study engaging professional translators in PE tasks using MTQE. To assess the translators’ cognitive load we measure their productivity both in terms of time and effort (keystrokes) in three different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that good MTQE information can improve post-editing efficiency and decrease the cognitive load on translators. This is especially true for cases with low MT quality.

scholarly journals KUALITAS HASIL TERJEMAHAN GOOGLE TRANSLATE DARI BAHASA ARAB KE BAHASA INDONESIA

2020 ◽  
Vol 3 (1) ◽  
pp. 127
Author(s):  
Hidayatul Khoiriyah
Keyword(s):  
Key Words ◽  
Machine Translation ◽  
Human Life ◽  
Target Language ◽  
Arabic Text ◽  
Grammatical Structure ◽  
Source Language ◽  
Translation Quality ◽  
Bahasa Indonesia

<p style="text-align: justify;"><em>The development of technology has a big impact on human life. The existence of a machine translation is the result of technological advancements that aim to facilitate humans in translating one language into another. The focus of this research is to examine the quality of the google translate machine in terms of vocabulary accuracy, clarity, and reasonableness of meaning. Data of mufradāt taken from several Arabic translation dictionaries, while the text is taken from the phenomenal work of Dr. Aidh Qorni in the book Lā Tahzan. The method used in this research is the translation critic method. </em></p><p style="text-align: justify;"><em>The results showed that in terms of the accuracy of vocabulary and terms, Google Translate has a good translation quality. In terms of clarity and reasonableness of meaning, google translate has not been able to transmit ideas from the source language well into the target language. Furthermore, in grammatical, the results of the google translate translation do not have a grammatical arrangement, the results of the google translate translation do not have a good grammatical structure and are by following the rules that applied in the target Indonesian language.</em></p><p style="text-align: justify;"><em>From the data, it shows that google translate should not be used as a basis for translating an Arabic text into Indonesian, especially in translating verses of the Qur'</em><em>ā</em><em>n and Hadīts. A beginner translator should prefer a dictionary rather than using google translate to effort and improve the ability to translate.</em></p><p style="text-align: justify;"><strong><em>Key Words: Translation, Google Translate, Arabic</em></strong></p>

scholarly journals Lost in Machine Translation: Contextual Linguistic Uncertainty

2019 ◽  
pp. 129-144
Author(s):  
Anton Sukhoverkhov ◽  
Dorothy DeWitt ◽  
Ioannis Manasidi ◽  
Keiko Nitta ◽  
Vladimir Krstić
Keyword(s):  
Machine Translation ◽  
Native Speakers ◽  
Open Systems ◽  
Test Results ◽  
Translation Quality ◽  
Different Types ◽  
Idiomatic Phrase ◽  
National Languages ◽  
Translation Systems

The article considers the issues related to the semantic, grammatical, stylistic and technical difficulties currently present in machine translation and compares its four main approaches: Rule-based (RBMT), Corpora-based (CBMT), Neural (NMT), and Hybrid (HMT). It also examines some "open systems", which allow the correction or augmentation of content by the users themselves ("crowdsourced translation"). The authors of the article, native speakers presenting different countries (Russia, Greece, Malaysia, Japan and Serbia), tested the translation quality of the most representative phrases from the English, Russian, Greek, Malay and Japanese languages by using different machine translation systems: PROMT (RBMT), Yandex. Translate (HMT) and Google Translate (NMT). The test results presented by the authors show low "comprehension level" of semantic, linguistic and pragmatic contexts of translated texts, mistranslations of rare and culture-specific words,unnecessary translation of proper names, as well as a low rate of idiomatic phrase and metaphor recognition. It is argued that the development of machine translation requires incorporation of literal, conceptual, and content-and-contextual forms of meaning processing into text translation expansion of metaphor corpora and contextological dictionaries, and implementation of different types and styles of translation, which take into account gender peculiarities, specific dialects and idiolects of users. The problem of untranslatability ('linguistic relativity') of the concepts, unique to a particular culture, has been reviewed from the perspective of machine translation. It has also been shown, that the translation of booming Internet slang, where national languages merge with English, is almost impossible without human correction.

scholarly journals Methodology for the Evaluation of Machine Translation Quality

2021 ◽  
Vol 1 (1) ◽  
pp. 124-133
Author(s):  
Ani Ananyan ◽  
Roza Avagyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Translation ◽  
Evaluation Method ◽  
Evaluation Process ◽  
Translation System ◽  
Translation Quality ◽  
Automatic Translation ◽  
Methods Of Evaluation ◽  
Translation Systems ◽  
Widespread Dissemination

Along with the development and widespread dissemination of translation by artificial intelligence, it is becoming increasingly important to continuously evaluate and improve its quality and to use it as a tool for the modern translator. In our research, we compared five sentences translated from Armenian into Russian and English by Google Translator, Yandex Translator and two models of the translation system of the Armenian company Avromic to find out how effective these translation systems are when working in Armenian. It was necessary to find out how effective it would be to use them as a translation tool and in the learning process by further editing the translation. As there is currently no comprehensive and successful method of human metrics for machine translation, we have developed our own evaluation method and criteria by studying the world's most well-known methods of evaluation for automatic translation. We have used the post-editorial distance evaluation criterion as well. In the example of one sentence in the article, we have presented in detail the evaluation process according to the selected and developed criteria. At the end we have presented the results of the research and made appropriate conclusions.

Grammatical Difficulties of Automatic Translation of Scientific and Technical Literature

2019 ◽  
Vol 26 (27) ◽  
pp. 134-141
Author(s):  
Aliona Kolesnichenko ◽  
Natalya Zhmayeva
Keyword(s):  
Key Words ◽  
Technical Literature ◽  
Automatic Translation ◽  
Advantages And Disadvantages ◽  
Grammatical Errors ◽  
Translation Service

The article is devoted to the analysis of grammatical difficulties encountered in the process of automatic translation. The paper discusses the advantages and disadvantages of the SDL Trados automatic translation service. The types of grammatical errors when translating scientific and technical texts in SDL Trados are classified, the ways of overcoming them are outlined. Key words: scientific and technical literature, automatic translation, grammatical difficulties.

scholarly journals Recurrent Stacking of Layers for Compact Neural Machine Translation Models

2019 ◽  
Vol 33 ◽  
pp. 6292-6299 ◽  
Author(s):  
Raj Dabre ◽  
Atsushi Fujita
Keyword(s):  
Machine Translation ◽  
Single Layer ◽  
Training Data ◽  
Neural Machine Translation ◽  
Parallel Corpora ◽  
Translation Quality ◽  
Sequence Generation ◽  
Sequence Modeling ◽  
Back Translation

In encoder-decoder based sequence-to-sequence modeling, the most common practice is to stack a number of recurrent, convolutional, or feed-forward layers in the encoder and decoder. While the addition of each new layer improves the sequence generation quality, this also leads to a significant increase in the number of parameters. In this paper, we propose to share parameters across all layers thereby leading to a recurrently stacked sequence-to-sequence model. We report on an extensive case study on neural machine translation (NMT) using our proposed method, experimenting with a variety of datasets. We empirically show that the translation quality of a model that recurrently stacks a single-layer 6 times, despite its significantly fewer parameters, approaches that of a model that stacks 6 different layers. We also show how our method can benefit from a prevalent way for improving NMT, i.e., extending training data with pseudo-parallel corpora generated by back-translation. We then analyze the effects of recurrently stacked layers by visualizing the attentions of models that use recurrently stacked layers and models that do not. Finally, we explore the limits of parameter sharing where we share even the parameters between the encoder and decoder in addition to recurrent stacking of layers.

scholarly journals Improving Translation Quality By Using Ensemble Approach

2018 ◽  
Vol 8 (6) ◽  
pp. 3512-3514
Author(s):  
D. Chopra ◽  
N. Joshi ◽  
I. Mathur
Keyword(s):  
Machine Translation ◽  
Open Problem ◽  
Current Paper ◽  
Translation Quality ◽  
Ensemble Approach ◽  
Baseline Approach

Machine translation (MT) has been a topic of great research during the last sixty years, but, improving its quality is still considered an open problem. In the current paper, we will discuss improvements in MT quality by the use of the ensemble approach. We performed MT from English to Hindi using 6 MT different engines described in this paper. We found that the quality of MT is improved by using a combination of various approaches as compared to the simple baseline approach for performing MT from source to target text.

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