Impact of the Google Translate Machine Translation System on the Quality of Training Student Translators

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.

Estimating Machine Translation Quality of Any Input Sentence

International Journal of Asian Language Processing ◽

10.1142/s2717554520500022 ◽

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 ◽

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.

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

MATEC Web of Conferences ◽

10.1051/matecconf/201925203006 ◽

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 ◽

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.

Word Sense Disambiguation for Improving the Quality of Machine Translation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.981.153 ◽

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.

Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i3.pp1059-1071 ◽

2016 ◽

Vol 6 (3) ◽

pp. 1059 ◽

Cited By ~ 1

Author(s):

K. Jaya ◽

Deepa Gupta

Keyword(s):

Machine Translation ◽

Research Work ◽

Translation System ◽

Indian Languages ◽

Translation Quality ◽

Parallel Data ◽

Best Parameter

Even though lot of Statistical Machine Translation(SMT) research work is happening for English-Hindi language pair, there is no effort done to standardize the dataset. Each of the research work uses different dataset, different parameters and different number of sentences during various phases of translation resulting in varied translation output. So comparing these models, understand the result of these models, to get insight into corpus behavior for these models, regenerating the result of these research work becomes tedious. This necessitates the need for standardization of dataset and to identify the common parameter for the development of model. The main contribution of this paper is to discuss an approach to standardize the dataset and to identify the best parameter which in combination gives best performance. It also investigates a novel corpus augmentation approach to improve the translation quality of English-Hindi bidirectional statistical machine translation system. This model works well for the scarce resource without incorporating the external parallel data corpus of the underlying language. This experiment is carried out using Open Source phrase-based toolkit Moses. Indian Languages Corpora Initiative (ILCI) Hindi-English tourism corpus is used. With limited dataset, considerable improvement is achieved using the corpus augmentation approach for the English-Hindi bidirectional SMT system.

Developing Statistical Machine Translation System for English and Nigerian Languages

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2018/v1i424761 ◽

2018 ◽

pp. 1-8

Author(s):

Ignatius Ikechukwu Ayogu ◽

Adebayo Olusola Adetunmbi ◽

Bolanle Adefowoke Ojokoh

Keyword(s):

Error Analysis ◽

Machine Translation ◽

Translation System ◽

Parallel Corpora ◽

Translation Tools ◽

Global Demand ◽

Translation Systems

The global demand for translation and translation tools currently surpasses the capacity of available solutions. Besides, there is no one-solution-fits-all, off-the-shelf solution for all languages. Thus, the need and urgency to increase the scale of research for the development of translation tools and devices continue to grow, especially for languages suffering under the pressure of globalisation. This paper discusses our experiments on translation systems between English and two Nigerian languages: Igbo and Yorùbá. The study is setup to build parallel corpora, train and experiment English-to-Igbo, (), English-to-Yorùbá, () and Igbo-to-Yorùbá, () phrase-based statistical machine translation systems. The systems were trained on parallel corpora that were created for each language pair using text from the religious domain in the course of this research. A BLEU score of 30.04, 29.01 and 18.72 respectively was recorded for the English-to-Igbo, English-to-Yorùbá and Igbo-to-Yorùbá MT systems. An error analysis of the systems’ outputs was conducted using a linguistically motivated MT error analysis approach and it showed that errors occurred mostly at the lexical, grammatical and semantic levels. While the study reveals the potentials of our corpora, it also shows that the size of the corpora is yet an issue that requires further attention. Thus an important target in the immediate future is to increase the quantity and quality of the data.

Exploration of Corpus Augmentation Approach for English-Hindi Bidirectional Statistical Machine Translation System

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i3.8904 ◽

2016 ◽

Vol 6 (3) ◽

pp. 1059 ◽

Cited By ~ 3

Author(s):

K. Jaya ◽

Deepa Gupta

Keyword(s):

Machine Translation ◽

Research Work ◽

Translation System ◽

Indian Languages ◽

Translation Quality ◽

Parallel Data ◽

Best Parameter

Even though lot of Statistical Machine Translation(SMT) research work is happening for English-Hindi language pair, there is no effort done to standardize the dataset. Each of the research work uses different dataset, different parameters and different number of sentences during various phases of translation resulting in varied translation output. So comparing these models, understand the result of these models, to get insight into corpus behavior for these models, regenerating the result of these research work becomes tedious. This necessitates the need for standardization of dataset and to identify the common parameter for the development of model. The main contribution of this paper is to discuss an approach to standardize the dataset and to identify the best parameter which in combination gives best performance. It also investigates a novel corpus augmentation approach to improve the translation quality of English-Hindi bidirectional statistical machine translation system. This model works well for the scarce resource without incorporating the external parallel data corpus of the underlying language. This experiment is carried out using Open Source phrase-based toolkit Moses. Indian Languages Corpora Initiative (ILCI) Hindi-English tourism corpus is used. With limited dataset, considerable improvement is achieved using the corpus augmentation approach for the English-Hindi bidirectional SMT system.

Extraction of multi-word expressions from small parallel corpora

Natural Language Engineering ◽

10.1017/s1351324912000101 ◽

2012 ◽

Vol 18 (4) ◽

pp. 549-573 ◽

Cited By ~ 8

Author(s):

YULIA TSVETKOV ◽

SHULY WINTNER

Keyword(s):

Machine Translation ◽

Simple Algorithm ◽

Search Space ◽

Translation System ◽

Parallel Corpora ◽

Source Language ◽

Extraction Algorithm ◽

Machine Translation System

AbstractWe present a general, novel methodology for extracting multi-word expressions (MWEs) of various types, along with their translations, from small, word-aligned parallel corpora. Unlike existing approaches, we focus on misalignments; these typically indicate expressions in the source language that are translated to the target in a non-compositional way. We introduce a simple algorithm that proposes MWE candidates based on such misalignments, relying on 1:1 alignments as anchors that delimit the search space. We use a large monolingual corpus to rank and filter these candidates. Evaluation of the quality of the extraction algorithm reveals significant improvements over naïve alignment-based methods. The extracted MWEs, with their translations, are used in the training of a statistical machine translation system, showing a small but significant improvement in its performance.

Experimental study of the impact of using a neural machine translation engine on the quality of translation of texts in the field of pharmacognosy

10.26565/2227-8877-2020-92-15 ◽

2020 ◽

Keyword(s):

Experimental Study ◽

Machine Translation ◽

Information And Communication Technologies ◽

Positive Impact ◽

Translation System ◽

Important Place ◽

Neural Machine Translation ◽

The Impact

The article is devoted to the study of the impact of using the neural machine translation system Google Translate on the quality of translation of texts in the field of pharmacognosy. At the present stage, the work of a translator is impossible to imagine without the use of information and communication technologies, an important place among which is attributed to machine translation. It is considered that neural machine translation systems perform translation at a fairly high level, so that its use by a human translator can have a positive impact. That is why the aim of the study was to conduct an experiment to determine the impact of using a neural machine translation system on the quality of translation of texts in the field of pharmacognosy in terms of the number of errors and correctness of translating terminology. The article formulates a research hypothesis, describes the text chosen to conduct the study and the neural machine translation system, which was selected for this purpose, discloses the procedure for estimating the number of errors in translations and calculating the percentage of correctness of translating terminology, provides quantitative experimental data, and the results are illustrated in tables and drawings. The experimental study was conducted in the first semester of the 2020/2021 academic year (September) on the basis of an excerpt from a text in the field of pharmacognosy, which was translated by the neural machine translation system Google Translate and a translation student of the bachelor’s level. Both translations were checked in terms of quantity and quality (types) of errors, as well as in terms of correctness of translating domain-specific terminology. The results refuted our hypothesis, as the translation performed by the neural machine translation system Google Translate was worse, both in terms of the number of errors and the percentage of correctness of translating terminology as compared to the results demonstrated by the student.

A Rule Based Approach for Translation of Causative Construction of English and Malayalam for the Development of Prototype for Malayalam to English and English To Malayalam Bilingual Machine Translation System

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.36.24134 ◽

2018 ◽

Vol 7 (4.36) ◽

pp. 542

Author(s):

T. K. Bijimol ◽

John T. Abraham

Keyword(s):

Machine Translation ◽

Translation System ◽

Indian Languages ◽

Rule Based ◽

Rule Based System ◽

Rule Based Approach ◽

Causative Construction ◽

Translation Systems

Malayalam is one of the Indian languages and it is a highly agglutinative and morphologically rich. These linguistic specialties of Malayalam determine the quality of all kinds of Malayalam machine translation systems. Causative sentences translations in Malayalam to English and English to Malayalam were analysed using Google Translation System and identified that causative sentence translation in these languages is not up to the mark. This paper discusses the concept and method of causative sentence handling in Malayalam to English and English to Malayalam Machine Translation Systems. A Rule-based system is proposed here to handle the causative sentence in both languages.