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 Questing for Quality Estimation A User Study

2017 ◽  
Vol 108 (1) ◽  
pp. 343-354 ◽  
Author(s):  
Carla Parra Escartín ◽  
Hanna Béchara ◽  
Constantin Orăsan
Keyword(s):  
Machine Translation ◽  
User Study ◽  
Project Managers ◽  
Impact Study ◽  
Field Of Study ◽  
Quality Estimation ◽  
Editing Efficiency ◽  
Translation Quality ◽  
The Cost

AbstractPost-Editing of Machine Translation (MT) has become a reality in professional translation workflows. In order to optimize the management of projects that use post-editing and avoid underpayments and mistrust from professional translators, effective tools to assess the quality of Machine Translation (MT) systems need to be put in place. One field of study that could address this problem is Machine Translation Quality Estimation (MTQE), which aims to determine the quality of MT without an existing reference. Accurate and reliable MTQE can help project managers and translators alike, as it would allow estimating more precisely the cost of post-editing projects in terms of time and adequate fares by discarding those segments that are not worth post-editing (PE) and have to be translated from scratch.In this paper, we report on the results of an impact study which engages professional translators in PE tasks using MTQE. We measured translators’ productivity in different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that QE information, when accurate, improves post-editing efficiency.

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.

scholarly journals An Oblivious Approach to Machine Translation Quality Estimation

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2090
Author(s):  
Itamar Elmakias ◽  
Dan Vilenchik
Keyword(s):  
Machine Translation ◽  
Accurate Method ◽  
Target Sentence ◽  
Expert Evaluation ◽  
Human Judgment ◽  
Quality Estimation ◽  
Translation Quality ◽  
Baseline System ◽  
Large Corpus

Machine translation (MT) is being used by millions of people daily, and therefore evaluating the quality of such systems is an important task. While human expert evaluation of MT output remains the most accurate method, it is not scalable by any means. Automatic procedures that perform the task of Machine Translation Quality Estimation (MT-QE) are typically trained on a large corpus of source–target sentence pairs, which are labeled with human judgment scores. Furthermore, the test set is typically drawn from the same distribution as the train. However, recently, interest in low-resource and unsupervised MT-QE has gained momentum. In this paper, we define and study a further restriction of the unsupervised MT-QE setting that we call oblivious MT-QE. Besides having no access no human judgment scores, the algorithm has no access to the test text’s distribution. We propose an oblivious MT-QE system based on a new notion of sentence cohesiveness that we introduce. We tested our system on standard competition datasets for various language pairs. In all cases, the performance of our system was comparable to the performance of the non-oblivious baseline system provided by the competition organizers. Our results suggest that reasonable MT-QE can be carried out even in the restrictive oblivious setting.

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.

Dimensionality reduction methods for machine translation quality estimation

2013 ◽  
Vol 27 (3-4) ◽  
pp. 281-301 ◽  
Author(s):  
Jesús González-Rubio ◽  
J. Ramón Navarro-Cerdán ◽  
Francisco Casacuberta
Keyword(s):  
Dimensionality Reduction ◽  
Machine Translation ◽  
Quality Estimation ◽  
Translation Quality ◽  
Reduction Methods

Cardiac rehabilitation during COVID-19 pandemic – new challenges, new opportunities

2021 ◽  
Vol 14 (3) ◽  
pp. 215-221
Author(s):  
Maciej Janiszewski ◽  
Artur Mamcarz
Keyword(s):  
Secondary Prevention ◽  
Cardiac Rehabilitation ◽  
Second Phase ◽  
Mortality And Morbidity ◽  
Rehabilitation Programs ◽  
Rehabilitation Centers ◽  
New Challenges ◽  
Artery Disease

The role of comprehensive cardiac rehabilitation (CCR) is well established in the secondary prevention of cardiovascular diseases such as coronary artery disease and heart failure. Many clinical trials demonstrated effectiveness of CCR in improving exercise capacity, quality of life, and in reducing cardiovascular mortality and morbidity. However, even before the era of the COVID-19 pandemic comprehensive cardiac rehabilitation program’s implementation, especially the second phase, had many barriers. One of the main reasons for not attending in second phase of CCR was lack of transportation from patient’s home to rehabilitation centers. Additionally, in recent months COVID-19 pandemic has led to closure of many cardiac rehabilitation centres resulting in many eligible patients unable to participate in the optimisation of secondary prevention. During the coronavirus disease-2019 pandemic, hybrid telerehabilitation has become the leading solution in the cardiac rehabilitation programs. The present paper contains key information about structures, effectives and safety of hybrid telerehabilitation during the COVID-19 era.

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.

scholarly journals Knowledge Graphs Enhanced Neural Machine Translation

2020 ◽  
Author(s):  
Yang Zhao ◽  
Jiajun Zhang ◽  
Yu Zhou ◽  
Chengqing Zong
Keyword(s):  
Machine Translation ◽  
Semantic Space ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Structured Information ◽  
Knowledge Graphs ◽  
Japanese Translation

Knowledge graphs (KGs) store much structured information on various entities, many of which are not covered by the parallel sentence pairs of neural machine translation (NMT). To improve the translation quality of these entities, in this paper we propose a novel KGs enhanced NMT method. Specifically, we first induce the new translation results of these entities by transforming the source and target KGs into a unified semantic space. We then generate adequate pseudo parallel sentence pairs that contain these induced entity pairs. Finally, NMT model is jointly trained by the original and pseudo sentence pairs. The extensive experiments on Chinese-to-English and Englishto-Japanese translation tasks demonstrate that our method significantly outperforms the strong baseline models in translation quality, especially in handling the induced entities.

scholarly journals MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation

2019 ◽  
Vol 28 (3) ◽  
pp. 447-453 ◽  
Author(s):  
Sainik Kumar Mahata ◽  
Dipankar Das ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Target Language ◽  
Data Sets ◽  
Shared Task ◽  
Automatic Translation ◽  
External Data ◽  
Statistical Mt

Abstract Machine translation (MT) is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks (RNNs) over traditional statistical MT (SMT). We compare the performance of the phrase table of SMT to the performance of the proposed RNN and in turn improve the quality of the MT output. This work has been done as a part of the shared task problem provided by the MTIL2017. We have constructed the traditional MT model using Moses toolkit and have additionally enriched the language model using external data sets. Thereafter, we have ranked the phrase tables using an RNN encoder-decoder module created originally as a part of the GroundHog project of LISA lab.

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