scholarly journals TRANSLATION OF ENGLISH TASKS INTO INDONESIAN THROUGH ONLINE MACHINE TRANSLATION PROGRAM

2017 ◽  
Vol 4 (1) ◽  
pp. 103
Author(s):  
Emzir Emzir ◽  
Ninuk Lustyantie ◽  
Akbar Akbar
Keyword(s):  
Machine Translation ◽  
Language Translation ◽  
Doctoral Program ◽  
Deep Understanding ◽  
Language Education ◽  
Target Language ◽  
State University ◽  
Optimum Result ◽  
Source Language ◽  
Different Cultures

The objective of this research is to obtain a deep understanding about the online machine translation of graduate students in the Language Education Doctoral Program of State University of Jakarta, Indonesia, from source language to target language in order to achieve equivalence in the subject of Language Translation and Education. The approach used is qualitative approach with ethnography method. The translation process is conducted by writing down words or copying-pasting sentences to be translated and then those words/sentences will be automatically translated by machine translation. A repetitive edit, revision and correction process shall be first performed in order to get an optimum result i.e. translated sentences are equal in textual and meanings. The deviations occur due to inaccurate equivalents caused by different cultures between the source language and target language as well as the scope of translated language scientific field. The used strategy is a literal translation. Based on the research results, the translation of English tasks to Indonesian through the online translation program is very useful to facilitate the students’ lecturing process in completing their tasks.

scholarly journals A Study of Neural Machine Translation from Chinese to Urdu

2020 ◽  
Vol 2 (4) ◽  
pp. 28
Author(s):  
. Zeeshan
Keyword(s):  
Machine Translation ◽  
Chinese Language ◽  
Language Translation ◽  
Target Language ◽  
Foreign Languages ◽  
Neural Machine Translation ◽  
Source Language ◽  
Great Progress ◽  
Score Method ◽  
Translation Methods

Machine Translation (MT) is used for giving a translation from a source language to a target language. Machine translation simply translates text or speech from one language to another language, but this process is not sufficient to give the perfect translation of a text due to the requirement of identification of whole expressions and their direct counterparts. Neural Machine Translation (NMT) is one of the most standard machine translation methods, which has made great progress in the recent years especially in non-universal languages. However, local language translation software for other foreign languages is limited and needs improving. In this paper, the Chinese language is translated to the Urdu language with the help of Open Neural Machine Translation (OpenNMT) in Deep Learning. Firstly, a Chineseto Urdu language sentences datasets were established and supported with Seven million sentences. After that, these datasets were trained by using the Open Neural Machine Translation (OpenNMT) method. At the final stage, the translation was compared to the desired translation with the help of the Bleu Score Method.

scholarly journals Knowledge Graphs Effectiveness in Neural Machine Translation Improvement

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Benyamin Ahmadnia ◽  
Bonnie J. Dorr ◽  
Parisa Kordjamshidi
Keyword(s):  
Machine Translation ◽  
Semantic Representation ◽  
Language Translation ◽  
Semantic Relations ◽  
Training Data ◽  
Target Language ◽  
Neural Machine Translation ◽  
Source Language ◽  
Knowledge Graphs ◽  
Unknown Words

Neural Machine Translation (NMT) systems require a massive amount of Maintaining semantic relations between words during the translation process yields more accurate target-language output from Neural Machine Translation (NMT). Although difficult to achieve from training data alone, it is possible to leverage Knowledge Graphs (KGs) to retain source-language semantic relations in the corresponding target-language translation. The core idea is to use KG entity relations as embedding constraints to improve the mapping from source to target. This paper describes two embedding constraints, both of which employ Entity Linking (EL)---assigning a unique identity to entities---to associate words in training sentences with those in the KG: (1) a monolingual embedding constraint that supports an enhanced semantic representation of the source words through access to relations between entities in a KG; and (2) a bilingual embedding constraint that forces entity relations in the source-language to be carried over to the corresponding entities in the target-language translation. The method is evaluated for English-Spanish translation exploiting Freebase as a source of knowledge. Our experimental results show that exploiting KG information not only decreases the number of unknown words in the translation but also improves translation quality.

scholarly journals Language To Language Translation System

2020 ◽  
pp. 289-293
Author(s):  
Ms Pratheeksha ◽  
Pratheeksha Rai ◽  
Ms Vijetha
Keyword(s):  
Speech Recognition ◽  
Machine Translation ◽  
Automatic Speech Recognition ◽  
Speech Synthesis ◽  
Language Translation ◽  
Target Language ◽  
Translation System ◽  
Text To Speech ◽  
Source Language ◽  
Text To Speech Synthesis

The system used in Language to Language Translation is the phrases spoken in one language are immediately spoken in other language by the device. Language to Language Translation is a three steps software process which includes Automatic Speech Recognition, Machine Translation and Voice Synthesis. Language to Language system includes the major speech translation projects using different approaches for Speech Recognition, Translation and Text to Speech synthesis highlighting the major pros and cons for the approach being used. Language translation is a process that takes the conversational phrase in one language as an input and translated speech phrases in another language as the output. The three components of language-to-language translation are connected in a sequential order. Automatic Speech Recognition (ASR) is responsible for converting the spoken phrases of source language to the text in the same language followed by machine translation which translates the source language to next target language text and finally the speech synthesizer is responsible for text to speech conversion of target language.

scholarly journals SOURCE LANGUAGE VERSUS TARGET LANGUAGE TRANSLATION PROCEDURES

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
AKHMAD BAIHAQI
Keyword(s):  
Qualitative Content Analysis ◽  
Language Translation ◽  
Deep Understanding ◽  
Analysis Data ◽  
Target Language ◽  
Study Program ◽  
Training And Education ◽  
Source Language ◽  
Education College ◽  
Teachers Training

The objective of the research was to gain deep understanding of the translation procedures that occurred at the students’ translation texts from English to Indonesian language in English Study Program of Teachers Training and Education College of Syekh Manshur in 2017. This research was conducted through qualitative content analysis. Data of the research were taken from the documentation of students’ translation text; then, they were analyzed through the model of inductive category development. The result of the research showed that from total of 18 translation texts that had been analyzed, it was found there were 9 texts or 49.99 % applied source language emphasized procedures, and 3 texts or 16.66 % applied target language emphasized procedures. The research concludes that the existence of translation procedures in the translation process depends on either a problem in translating or the intention of naturalness.  

scholarly journals Optimizing Tokenization Choice for Machine Translation across Multiple Target Languages

2017 ◽  
Vol 108 (1) ◽  
pp. 257-269 ◽  
Author(s):  
Nasser Zalmout ◽  
Nizar Habash
Keyword(s):  
Machine Translation ◽  
Performance Enhancement ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Source Language ◽  
Context Variable ◽  
Significant Performance ◽  
Morphologically Rich Languages ◽  
Target Languages ◽  
Language Text

AbstractTokenization is very helpful for Statistical Machine Translation (SMT), especially when translating from morphologically rich languages. Typically, a single tokenization scheme is applied to the entire source-language text and regardless of the target language. In this paper, we evaluate the hypothesis that SMT performance may benefit from different tokenization schemes for different words within the same text, and also for different target languages. We apply this approach to Arabic as a source language, with five target languages of varying morphological complexity: English, French, Spanish, Russian and Chinese. Our results show that different target languages indeed require different source-language schemes; and a context-variable tokenization scheme can outperform a context-constant scheme with a statistically significant performance enhancement of about 1.4 BLEU points.

scholarly journals Controlling Neural Machine Translation Formality with Synthetic Supervision

2020 ◽  
Vol 34 (05) ◽  
pp. 8568-8575
Author(s):  
Xing Niu ◽  
Marine Carpuat
Keyword(s):  
Machine Translation ◽  
Target Language ◽  
Sentence Pair ◽  
English Sentence ◽  
Neural Machine Translation ◽  
Source Language ◽  
Training Scheme ◽  
Training Examples ◽  
Language Content ◽  
Missing Element

This work aims to produce translations that convey source language content at a formality level that is appropriate for a particular audience. Framing this problem as a neural sequence-to-sequence task ideally requires training triplets consisting of a bilingual sentence pair labeled with target language formality. However, in practice, available training examples are limited to English sentence pairs of different styles, and bilingual parallel sentences of unknown formality. We introduce a novel training scheme for multi-task models that automatically generates synthetic training triplets by inferring the missing element on the fly, thus enabling end-to-end training. Comprehensive automatic and human assessments show that our best model outperforms existing models by producing translations that better match desired formality levels while preserving the source meaning.1

A Knowledge-Based Machine Translation Using AI Technique

2018 ◽  
Vol 6 (3) ◽  
pp. 79-92
Author(s):  
Sahar A. El-Rahman ◽  
Tarek A. El-Shishtawy ◽  
Raafat A. El-Kammar
Keyword(s):  
Machine Translation ◽  
Target Language ◽  
Translation System ◽  
Module Structure ◽  
Source Language ◽  
Word Category ◽  
Knowledge Based ◽  
Language Analysis ◽  
Transfer Rules

This article presents a realistic technique for the machine aided translation system. In this technique, the system dictionary is partitioned into a multi-module structure for fast retrieval of Arabic features of English words. Each module is accessed through an interface that includes the necessary morphological rules, which directs the search toward the proper sub-dictionary. Another factor that aids fast retrieval of Arabic features of words is the prediction of the word category, and accesses its sub-dictionary to retrieve the corresponding attributes. The system consists of three main parts, which are the source language analysis, the transfer rules between source language (English) and target language (Arabic), and the generation of the target language. The proposed system is able to translate, some negative forms, demonstrations, and conjunctions, and also adjust nouns, verbs, and adjectives according their attributes. Then, it adds the symptom of Arabic words to generate a correct sentence.

scholarly journals THEORETICAL AND PRACTICAL PECULIARITIES OF TRANSLATING CULTURE-SPECIFIC TERMS

2020 ◽  
Vol 74 (4) ◽  
pp. 494-497
Author(s):  
B. Mizamkhan ◽  
◽  
T. Kalibekuly ◽  
Keyword(s):  
Point Of View ◽  
Target Language ◽  
Mutual Understanding ◽  
Cultural Backgrounds ◽  
Source Language ◽  
Language Groups ◽  
Adequate Understanding ◽  
Cultural Connotations ◽  
Different Cultures ◽  
Cultural Linguistics

The term “culture-specific vocabulary” appeared in the 1980s. Problems of translating culture-specific terms from one language to another have always been a serious issue for translators. It causes even more problems if the languages being compared belong to different language groups and represent different cultures. Nevertheless, the study of culture-specific vocabulary helps to achieve the adequacy of translation, which in turn helps speakers of different languages ​​and cultures to achieve mutual understanding. The above emphasizes the relevance and timeliness of the study of translation from the point of view of cultural linguistics. This paper will examine the peculiarities of translating culture-specific terms from Kazakh into English. It provides different methods of translating cultural connotations, taking into account the ways of living and thinking, as well the historical and cultural backgrounds embedded in the source language (hereafter SL) and target language (hereafter TL). These methods will be analyzed using specific examples, originals and translations of such works as “The Path of Abai” by Mukhtar Auezov and “Nomads” by Ilyas Yessenberlin. Therefore, the main aim of the paper is to try to explain main approaches and theories needed for adequate understanding of different cultures through translation.

scholarly journals Query Expansion for Slovak to Bulgarian Language Machine Translation using Parallel Search

2021 ◽  
pp. 351-357
Author(s):  
VELISLAVA STOYKOVA ◽  
DANIELA MAJCHRAKOVA
Keyword(s):  
Machine Translation ◽  
Query Expansion ◽  
Statistical Approach ◽  
Semantic Relations ◽  
Target Language ◽  
Parallel Search ◽  
Keyword Query ◽  
Source Language ◽  
Standard Presentation ◽  
Standard Semantic

The paper presents results of the application of a statistical approach for Slovak to Bulgarian language machine translation. It uses Information Retrieval inspired search techniques and employs sever alalgorithmic steps of parallel statistical search with query expansion in Slovak-Bulgarian EUROPARL 7 Corpus using the Sketch Engine software and its scoring. The search includes the generation of concordances,collocations, word sketch differences, word sketches, and thesauri of the studied keyword (query) by using a statistical scoring, which is regarded as intermediate (inter-lingual) semantic standard presentation by means of which the studied keyword (from the source language) is mapped together with its possible translation equivalents (onto the target language. The results present the study of adjectival collocabillity in both Slovak and Bulgarian language from the corpus of political speech texts outlining the standard semantic relations based on the evaluation of statistical scoring. Finally, the advantages and shortcomings of the approach are discussed.

scholarly journals Translation Teaching Strategies For Arabic Language Education Students at The State Islamic Institute (IAIN) Ponorogo

ASALIBUNA ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Amir Mukminin
Keyword(s):  
Teaching Methods ◽  
Descriptive Analysis ◽  
Language Education ◽  
Arabic Language ◽  
Target Language ◽  
Language Competence ◽  
Translation Strategies ◽  
Source Language ◽  
Text Understanding ◽  
Semantic Translation

This study aims to explore teaching methods and strategies in teaching translation among lecturers at IAIN Ponorogo. In their teaching, the lecturers determine the high goals, that is being able to translate Arabic into Indonesian, and the reverse; However, students' language competence is not good enough to achieve that goal because translation is a work that must be supported by various types of competencies such as understanding text, understanding Arabic grammar, understanding methods and translation strategies, and so on. Student competencies are not in accordance with the specific goals in this education, and this fact is what leads lecturers to carry out effective teaching and use good types of teaching methods and strategies so that education is successful and students can achieve the goals. Researchers used a qualitative approach and explored descriptive facts such as written voices or individual speeches, contemplative traits and data sources. The data analysis method is descriptive analysis with three methods, namely data reduction and data presentation. The results of this study are 1) The method used in teaching translation: translation is word by word, because translation is done between separation by placing the translation under the source language in order to maintain word order. The word is translated as a word in a general sense. Pro translation because it produces contextual meaning of the source language into the target language appropriately. Therefore, translators are careful in translating cultural vocabulary and adjusting grammar. This method seeks to achieve the author's goals. 2) The strategy used is an expansion against the target, and the word element is an expansion in the target language, semantic translation, ordinal translation, transcription or semantic translation intersection. 3) And the learning outcomes obtained by students are good, academic results are not good. The result of the equation 70 (seventy) 

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