scholarly journals Human-Computer Interaction in Translation Activity: Fluency of Machine Translation

2021 ◽  
Vol 18 (1) ◽  
pp. 217-234
Author(s):  
Katarina Welnitzova ◽  
Barbara Jakubickova ◽  
Roman Králik
Keyword(s):  
Machine Translation ◽  
Social Life ◽  
Assessment Tool ◽  
Criminal Code ◽  
Translation System ◽  
Original Language ◽  
Significant Difference ◽  
Legal Texts ◽  
Back Translation

Digitalization is one of the key distinctive features of modern environment and social life. Nowadays more and more functions are transferred to the artificial mind. How effective is the replacement of human activity with computer activity? In the given article, this problem is solved by an example of integration of digital technologies into translation activities. It this paper, emphasis is placed on the quality of machine translation (MT) output of legal texts in the language pair English - Slovak. It studies a Criminal Code formulated in the Slovak language which was translated by a human translator into English and consequently via machine translation system Google Translate (GT) back into Slovak. The back-translation - translation of a translated text back into its original language - as a quality assessment tool to detect discrepancies, mistranslations and inevitable differences between the source text and the target text was used. The quality of MT output was evaluated according to Multidimensional Quality Metrics (MQM) standards with the focus on the dimension of Fluency. The multiple comparisons were applied to determine which issues (errors) in Fluency dimension differ from the others. A statistically significant difference is noticed between Agreement and other issues, as well as between Ambiguity and other issues. The errors in Agreement are related to the differences between the languages: English is considered mostly an analytic language, Slovak represents a synthetic language. The issues in the Ambiguity dimension correlate with the type of the text being examined, since legal texts are characterized by relatively complicated wording and numerous terms; moreover, accuracy and unambiguity need to be preserved. Generally, the MT output is able to provide users with basic information about the text. On the other hand, most of the segments need revision and/or correction; in such cases, human intervention and post-editing is necessary.

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.

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 Validation of the French version of the Wisconsin Quality of Life (WISQOL) questionnaire for patients with urolithiasis

2020 ◽  
Vol 15 (4) ◽  
Author(s):  
Naeem Bhojani ◽  
Ghizlane Moussaoui ◽  
David-Dan Nguyen ◽  
Mei Juan Trudel ◽  
Garo-Shant Topouzian ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Internal Consistency ◽  
Tertiary Care ◽  
Rank Correlation ◽  
French Version ◽  
Spearman’S Rank Correlation ◽  
Significant Difference ◽  
Back Translation ◽  
One Way Anova

Introduction: The Wisconsin Stone Quality of Life (WISQOL) questionnaire has been recently developed to objectively assess QOL in patients with urolithiasis. However, French version of the questionnaire was lacking. Therefore, the aim of the present study was to develop and validate the French version of this tool. Methods: The French version of the WISQOL (F-WISQOL) was developed in a multi-step process involving primary translation, back-translation and pilot testing amongst a group of patients (n=12). Urolithiasis patients from two tertiary care institutions were recruited into this study and completed 3 questionnaires: Perceived Stress Scale-10, medical history form and either the WISQOL or F-WISQOL. Internal consistency was assessed using Cronbach’s α and inter-domain associations were evaluated using Spearman’s rank correlation (r). One-way ANOVA was used to compare scores from the two groups (WISQOL and F-WISQOL). Results: A total of 210 patients were enrolled in this study; 68 in the WISQOL group and 148 in the F-WISQOL group. Internal consistency was high for all domains in both groups (F-WISQOL: 0.924-0.970; WISQOL: 0.888-0.965). No statistically significant difference was found between the two groups’ scores. Inter-domain association, measured by Spearman correlation, was moderate to very strong between all of the domains in the F-WISQOL. Values ranged from r=0.676-0.915, with acceptable correlation between D1, D2 and D3, but weaker correlation between D4 (vitality) and the 3 other domains r=0.676-0.729. Conclusions: In the present study, the French version of the WISQOL questionnaire (F-WISQOL) was validated at two academic institutions.

Efficient Phrase Table pruning for Hindi to English machine translation through syntactic and marker-based filtering and hybrid similarity measurement

2018 ◽  
Vol 25 (1) ◽  
pp. 171-210
Author(s):  
NILADRI CHATTERJEE ◽  
SUSMITA GUPTA
Keyword(s):  
Machine Translation ◽  
Syntactic Structure ◽  
Verbal Learning ◽  
Target Language ◽  
Maximum Efficiency ◽  
Similarity Measurement ◽  
Translation System ◽  
Training Corpus ◽  
Generative Process

AbstractFor a given training corpus of parallel sentences, the quality of the output produced by a translation system relies heavily on the underlying similarity measurement criteria. A phrase-based machine translation system derives its output through a generative process using a Phrase Table comprising source and target language phrases. As a consequence, the more effective the Phrase Table is, in terms of its size and the output that may be derived out of it, the better is the expected outcome of the underlying translation system. However, finding the most similar phrase(s) from a given training corpus that can help generate a good quality translation poses a serious challenge. In practice, often there are many parallel phrase entries in a Phrase Table that are either redundant, or do not contribute to the translation results effectively. Identifying these candidate entries and removing them from the Phrase Table will not only reduce the size of the Phrase Table, but should also help in improving the processing speed for generating the translations. The present paper develops a scheme based on syntactic structure and the marker hypothesis (Green 1979, The necessity of syntax markers: two experiments with artificial languages, Journal of Verbal Learning and Behavior) for reducing the size of a Phrase Table, without compromising much on the translation quality of the output, by retaining the non-redundant and meaningful parallel phrases only. The proposed scheme is complemented with an appropriate similarity measurement scheme to achieve maximum efficiency in terms of BLEU scores. Although designed for Hindi to English machine translation, the overall approach is quite general, and is expected to be easily adaptable for other language pairs as well.

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.

scholarly journals Neural machine translation system for the Kazakh language based on synthetic corpora

2019 ◽  
Vol 252 ◽  
pp. 03006
Author(s):  
Ualsher Tukeyev ◽  
Aidana Karibayeva ◽  
Balzhan Abduali
Keyword(s):  
Machine Translation ◽  
Training Data ◽  
Translation System ◽  
Natural Languages ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Parallel Data ◽  
Machine Translation System ◽  
Turkic Languages

The lack of big parallel data is present for the Kazakh language. This problem seriously impairs the quality of machine translation from and into Kazakh. This article considers the neural machine translation of the Kazakh language on the basis of synthetic corpora. The Kazakh language belongs to the Turkic languages, which are characterised by rich morphology. Neural machine translation of natural languages requires large training data. The article will show the model for the creation of synthetic corpora, namely the generation of sentences based on complete suffixes for the Kazakh language. The novelty of this approach of the synthetic corpora generation for the Kazakh language is the generation of sentences on the basis of the complete system of suffixes of the Kazakh language. By using generated synthetic corpora we are improving the translation quality in neural machine translation of Kazakh-English and Kazakh-Russian pairs.

scholarly journals PERBEDAAN KUALITAS HIDUP PASIEN USIA LANJUT SEBELUM DAN SETELAH OPERASI KATARAK

2020 ◽  
Vol 4 (1) ◽  
pp. 63-68
Author(s):  
Adelia Lisnawati
Keyword(s):  
Quality Of Life ◽  
Visual Acuity ◽  
Elderly Patients ◽  
Cataract Surgery ◽  
Visual Function ◽  
Social Life ◽  
Significant Difference ◽  
Before And After ◽  
Eye Clinic

Cataract is a condition when the lens become cloudy and often occurs in elderly patients. Cataract is the leading cause of visual impairment and blindness in the world. Cataract can reduce productivity and social life, that will decrease the quality of life in elderly patients. It also reduces the visual acuity leading to decreasing visual function and the quality of life. This disease can change physical, cognitive and psychosocial life. This study aimed to analyze the difference of quality of life in elderly patients before and after cataract surgery at SMEC eye clinic in Samarinda. This study was observational analytic study. Data were taken from interview the patients with visual function questionnaire 14 (VFQ 14) and from the medical record of SMEC eye clinic in Samarinda. The results showed a significant difference of quality of life in elderly patients before and after cataract surgery (p = 0,000) with the mean score of quality of life before surgery (x̅= 63,65) was lower than after cataract surgery (x̅= 95,35) and there was significant improvement of the visual acuity after cataract surgery (p = 0,000). Based on these results it can be concluded that there were difference of quality of life in elderly patients before and after cataract surgery.

Word Sense Disambiguation for Improving the Quality of Machine Translation

2014 ◽  
Vol 981 ◽  
pp. 153-156
Author(s):  
Chun Xiang Zhang ◽  
Long Deng ◽  
Xue Yao Gao ◽  
Li Li Guo
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Translation System ◽  
Word Sense ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Machine Translation System

Word sense disambiguation is key to many application problems in natural language processing. In this paper, a specific classifier of word sense disambiguation is introduced into machine translation system in order to improve the quality of the output translation. Firstly, translation of ambiguous word is deleted from machine translation of Chinese sentence. Secondly, ambiguous word is disambiguated and the classification labels are translations of ambiguous word. Thirdly, these two translations are combined. 50 Chinese sentences including ambiguous words are collected for test experiments. Experimental results show that the translation quality is improved after the proposed method is applied.

scholarly journals MACHINE TRANSLATION IN THE FIELD OF LAW: A STUDY OF THE TRANSLATION OF ITALIAN LEGAL TEXTS INTO GERMAN

2019 ◽  
Vol 37 ◽  
pp. 117-153
Author(s):  
Eva WIESMANN
Keyword(s):  
Machine Translation ◽  
Translation System ◽  
Legal Language ◽  
Legal Text ◽  
Raising Awareness ◽  
Legal Texts ◽  
Different Types ◽  
Machine Translation System ◽  
Translation Pedagogy ◽  
Legal Expertise

With the advent of the neural paradigm, machine translation has made another leap in quality. As a result, its use by trainee translators has increased considerably, which cannot be disregarded in translation pedagogy. However, since legal texts have features that pose major challenges to machine translation, the question arises as to what extent machine translation is now capable of translating legal texts or at least certain types of legal text into another legal language well enough so that the post-editing effort is limited, and, consequently, whether a targeted use in translation pedagogy can be considered. In order to answer this question, DeepL Translator, a machine translation system, and MateCat, a CAT system that integrates machine translation, were tested. The test, undertaken at different times and without specific translation memories, provided for the translation of several legal texts of different types utilising both systems, and was followed by systematisation of errors and evaluation of translation results. The evaluation was carried out according to the following criteria: 1) comprehensibility and meaningfulness of the target text; and 2) correspondence between source and target text in consideration of the specific translation situation. Overall, the results are considered insufficient to give post-editing of machine-translated legal texts a bigger place in translation pedagogy. As the evaluation of the correspondence between source and target text was fundamentally worse than with regard to the meaningfulness of the target text, translation pedagogy should respond by raising awareness about differences between machine translation output and human translation in this field, and by improving translation approach and strengthening legal expertise.

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