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.

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 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.

Estimating Machine Translation Quality of Any Input Sentence

2020 ◽  
Vol 30 (01) ◽  
pp. 2050002
Author(s):  
Taichi Aida ◽  
Kazuhide Yamamoto
Keyword(s):  
Machine Translation ◽  
Evaluation Model ◽  
Joint Probability ◽  
Translation System ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Machine Translation System ◽  
Input Sentence ◽  
The One

Current methods of neural machine translation may generate sentences with different levels of quality. Methods for automatically evaluating translation output from machine translation can be broadly classified into two types: a method that uses human post-edited translations for training an evaluation model, and a method that uses a reference translation that is the correct answer during evaluation. On the one hand, it is difficult to prepare post-edited translations because it is necessary to tag each word in comparison with the original translated sentences. On the other hand, users who actually employ the machine translation system do not have a correct reference translation. Therefore, we propose a method that trains the evaluation model without using human post-edited sentences and in the test set, estimates the quality of output sentences without using reference translations. We define some indices and predict the quality of translations with a regression model. For the quality of the translated sentences, we employ the BLEU score calculated from the number of word [Formula: see text]-gram matches between the translated sentence and the reference translation. After that, we compute the correlation between quality scores predicted by our method and BLEU actually computed from references. According to the experimental results, the correlation with BLEU is the highest when XGBoost uses all the indices. Moreover, looking at each index, we find that the sentence log-likelihood and the model uncertainty, which are based on the joint probability of generating the translated sentence, are important in BLEU estimation.

IQ and Reading Comprehension in Translation Quality

2017 ◽  
Vol 5 (4) ◽  
pp. 48 ◽  
Author(s):  
Mohsen Askari ◽  
Azam Samadi Rahim
Keyword(s):  
Reading Comprehension ◽  
Quality Assessment ◽  
Translation Studies ◽  
Study Data ◽  
Significant Finding ◽  
Translation Quality ◽  
Determining Factors ◽  
Almost All ◽  
Raven's Advanced Progressive Matrices

Having a deeper understanding of determining factors in the quality of translation is in the interest of almost all scholars of translation studies. Students’ intelligence is being measured constantly in order to determine their aptitude for entering into different programs. However, in translation studies, the variable of intelligence quotient (IQ) has been curiously ignored among researchers. This study aimed to explore the strength of both IQ and reading comprehension in predicting translation quality among Iranian translation students.  A sample of forty-six translation students from Alborz University of Qazvin participated in this study. Data were collected using three tests including Raven’s Advanced Progressive Matrices, Colina’s (2008) componential translation quality rating scheme and the reading comprehension test of IELTS. The results show IQ test scores and reading comprehension significantly predict translation quality assessment. Surprisingly, the most significant finding is that IQ score is by far a better predictor of translation quality than reading comprehension. Overall, it is concluded that translation quality assessment is more of a deeper cognitive function than solely language process, which could lead to more research on cognitive aspects of translation.

scholarly journals Translating Subtitles of Becoming Jane Film: A Pragmatic Approach

2020 ◽  
Vol 6 (1) ◽  
pp. 17
Author(s):  
Arbain Arbain
Keyword(s):  
Speech Acts ◽  
Qualitative Method ◽  
Document Analysis ◽  
Pragmatic Approach ◽  
Focus Group Discussions ◽  
Group Discussions ◽  
Translation Quality ◽  
The World ◽  
Translation Techniques

Subtitling is an effective way to provide dialogues or narrative for a movie. The benefit is for people to enjoy the film even though its different from their native language. They enjoy movies over the world with different countries and styles by the dialogues translated. This research aims to know the strategy of responding to arguing in the file's subtitle titled Becoming Jane, to find out the translation techniques used by the translator, and to assess the translation quality in terms of the accuracy, acceptability, and readability. The research method is descriptive qualitative method with "Becoming Jane" movie and its subtitles as the data. Data were collected from document analysis and focus group discussions with the score of the accuracy, acceptability, and readability. The results indicate that the character used a strategy of agreeing, persisting, and complying. While six methods of translation were found, namely literal, modulation, established equivalence, borrowing, and adaptation. The quality of the translation of the strategy of responding to speech acts has been categorized as less accurate, less acceptable, and moderate in terms of readability

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