scholarly journals Efficiency of Machine Translation in Urban Discourse

2021 ◽  
pp. 87-98
Author(s):  
Svetlana Korolkova ◽  
◽  
Anna Novozhilova ◽  
Keyword(s):  
Machine Translation ◽  
Russian Language ◽  
Target Language ◽  
Translation System ◽  
Web Browser ◽  
Translation Quality ◽  
Machine Translation System ◽  
Language Quality ◽  
Urban Discourse ◽  
Website Content

This article aims to analyze the use of Yandex.Translate, an online machine translation system, in translating urban discourse texts on the web. The authors use integrative linguistic-and-pragmatic approach to assess machine translation quality in a global digital setting. The aim is to show the efficiency of a state-of-the-art machine translation system and to investigate its usefulness in practical application. The authors perform a detailed analysis of the Paris city website content, which is automatically translated from French into Russian with Yandex.Translate. The data selection is justified by the absence of official foreign versions of this website, which points to the need of machine translation engines integrated in a web browser. Less than 20% of the analysed machine-translated texts demonstrate high language quality, whereas 60% can be referred to as acceptable – the text preserves the meaning of the source but contains some errors and inaccuracies in the target language. About 20% of the machine-translated text contains blunders, which violate Russian language norms. It causes source text contents distortion and communication failures. In the end, a classification of the system errors is presented. It is also concluded that machine translation would substitute middle-skilled human translators in the future. However, the use of such systems will enforce standardisation and simplification of the target language.

scholarly journals Phrase Dependency Machine Translation with Quasi-Synchronous Tree-to-Tree Features

2014 ◽  
Vol 40 (2) ◽  
pp. 349-401 ◽  
Author(s):  
Kevin Gimpel ◽  
Noah A. Smith
Keyword(s):  
Machine Translation ◽  
Target Language ◽  
Translation System ◽  
World Systems ◽  
Translation Quality ◽  
Hard Constraints ◽  
Machine Translation System ◽  
Test Sets ◽  
Isomorphic Structure ◽  
Dependency Syntax

Recent research has shown clear improvement in translation quality by exploiting linguistic syntax for either the source or target language. However, when using syntax for both languages (“tree-to-tree” translation), there is evidence that syntactic divergence can hamper the extraction of useful rules (Ding and Palmer 2005 ). Smith and Eisner ( 2006 ) introduced quasi-synchronous grammar, a formalism that treats non-isomorphic structure softly using features rather than hard constraints. Although a natural fit for translation modeling, its flexibility has proved challenging for building real-world systems. In this article, we present a tree-to-tree machine translation system inspired by quasi-synchronous grammar. The core of our approach is a new model that combines phrases and dependency syntax, integrating the advantages of phrase-based and syntax-based translation. We report statistically significant improvements over a phrase-based baseline on five of seven test sets across four language pairs. We also present encouraging preliminary results on the use of unsupervised dependency parsing for syntax-based machine translation.

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.

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 Evaluating Machine Translation Quality Using Short Segments Annotations

2015 ◽  
Vol 103 (1) ◽  
pp. 85-110
Author(s):  
Matouš Macháček ◽  
Ondřej Bojar
Keyword(s):  
Machine Translation ◽  
Evaluation Method ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Translation Quality ◽  
Machine Translation System

Abstract We propose a manual evaluation method for machine translation (MT), in which annotators rank only translations of short segments instead of whole sentences. This results in an easier and more efficient annotation. We have conducted an annotation experiment and evaluated a set of MT systems using this method. The obtained results are very close to the official WMT14 evaluation results. We also use the collected database of annotations to automatically evaluate new, unseen systems and to tune parameters of a statistical machine translation system. The evaluation of unseen systems, however, does not work and we analyze the reasons

scholarly journals Predicting and Using a Pragmatic Component of Lexical Aspect

2016 ◽  
Vol 13 ◽  
Author(s):  
Sharid Loáiciga ◽  
Cristina Grisot
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Translation System ◽  
Automatic Annotation ◽  
Parallel Corpora ◽  
Linguistic Data ◽  
Lexical Aspect ◽  
Machine Translation System ◽  
Automatic Systems

This paper proposes a method for improving the results of a statistical Machine Translation system using boundedness, a pragmatic component of the verbal phrase’s lexical aspect. First, the paper presents manual and automatic annotation experiments for lexical aspect in English-French parallel corpora. It will be shown that this aspectual property is identified and classified with ease both by humans and by automatic systems. Second, Statistical Machine Translation experiments using the boundedness annotations are presented. These experiments show that the information regarding lexical aspect is useful to improve the output of a Machine Translation system in terms of better choices of verbal tenses in the target language, as well as better lexical choices. Ultimately, this work aims at providing a method for the automatic annotation of data with boundedness information and at contributing to Machine Translation by taking into account linguistic data.

scholarly journals Factors Behind the Effectiveness of an Unsupervised Neural Machine Translation System between Korean and Japanese

2021 ◽  
Vol 11 (16) ◽  
pp. 7662
Author(s):  
Yong-Seok Choi ◽  
Yo-Han Park ◽  
Seung Yun ◽  
Sang-Hun Kim ◽  
Kong-Joo Lee
Keyword(s):  
Machine Translation ◽  
Word Order ◽  
Target Language ◽  
Translation System ◽  
Generation Model ◽  
Neural Machine Translation ◽  
Language Differences ◽  
Machine Translation System ◽  
The Common ◽  
Morphological System

Korean and Japanese have different writing scripts but share the same Subject-Object-Verb (SOV) word order. In this study, we pre-train a language-generation model using a Masked Sequence-to-Sequence pre-training (MASS) method on Korean and Japanese monolingual corpora. When building the pre-trained generation model, we allow the smallest number of shared vocabularies between the two languages. Then, we build an unsupervised Neural Machine Translation (NMT) system between Korean and Japanese based on the pre-trained generation model. Despite the different writing scripts and few shared vocabularies, the unsupervised NMT system performs well compared to other pairs of languages. Our interest is in the common characteristics of both languages that make the unsupervised NMT perform so well. In this study, we propose a new method to analyze cross-attentions between a source and target language to estimate the language differences from the perspective of machine translation. We calculate cross-attention measurements between Korean–Japanese and Korean–English pairs and compare their performances and characteristics. The Korean–Japanese pair has little difference in word order and a morphological system, and thus the unsupervised NMT between Korean and Japanese can be trained well even without parallel sentences and shared vocabularies.

scholarly journals Hva er viktig for forståelse? Om maskinoversetting fra nordsamisk

2021 ◽  
Vol 11 (2) ◽  
pp. 489-501
Author(s):  
Trond Trosterud ◽  
Lene Antonsen
Keyword(s):  
Machine Translation ◽  
Text Comprehension ◽  
The Other ◽  
Translation System ◽  
Rule Based ◽  
Grammatical Analysis ◽  
Machine Translation System ◽  
Language Quality ◽  
Lexical Errors ◽  
Fluent Language

The article presents a rule-based machine translation system from Northern Sami to Norwegian. The grammatical analysis is done with Giellatekno and Divvun's North Sami program for analysis and translation. We have written the transfer component (transfer lexicon and grammatical rules) within the framework of the open machine translation system Apertium. The article contains an evaluation of translated text for two different domains. The translated texts score better on the presentation of the content than on fluent language. By classifying the errors into lexical, grammatical and pragmatic errors, we show that lexical errors are the most harmful for text comprehension. The other two types of errors give a poor language quality, but they have little effect on comprehension. The type of error that is the easiest to correct is the lexical, which is a promising conclusion for the development of a machine translation system for text comprehension.

scholarly journals Machine Translation System Using Deep Learning for English to Urdu

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Syed Abdul Basit Andrabi ◽  
Abdul Wahid
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Communication Technology ◽  
Target Language ◽  
Translation System ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Proposed Model ◽  
Learning Technique ◽  
Machine Translation System

Machine translation is an ongoing field of research from the last decades. The main aim of machine translation is to remove the language barrier. Earlier research in this field started with the direct word-to-word replacement of source language by the target language. Later on, with the advancement in computer and communication technology, there was a paradigm shift to data-driven models like statistical and neural machine translation approaches. In this paper, we have used a neural network-based deep learning technique for English to Urdu languages. Parallel corpus sizes of around 30923 sentences are used. The corpus contains sentences from English-Urdu parallel corpus, news, and sentences which are frequently used in day-to-day life. The corpus contains 542810 English tokens and 540924 Urdu tokens, and the proposed system is trained and tested using 70 : 30 criteria. In order to evaluate the efficiency of the proposed system, several automatic evaluation metrics are used, and the model output is also compared with the output from Google Translator. The proposed model has an average BLEU score of 45.83.

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.

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