A Lexical-Functional Model for Machine Translation of English Zero-place Predicators into Persian

Ahangar, Abbas Ali; Nader, Jahangiri; Mohammadpour, F

doi:10.5539/ijel.v2n3p2

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…Younesi Far (as cited in Ahangar et al [1]) planed and made a MT system based on Augmented Transition Networks /ATN. This system could generate a word for word translation of English sentences into Persian.…”

Section: The History Of English-persian Machine Translationmentioning

confidence: 99%

“…-An Investigation of the Translation Problems Incurred by English-Persian Machine Translation: 'Padideh, Pars, and Google Softwares by Komeili et al [11] that evaluates quality of translation produced by Padideh, Pars, and Google in translating simple, combined, and complicated sentences and idioms. -A lexical-Functional Model for Machine Translation of English Zero-Place Predicators into English by Ahangar et al [1] that demonstrates utilizing Lexical-Functional Grammar for a MT system which is designed for translation of some English zero-place predicators can produce a natural translation of them into Persian.…”

Section: The History Of English-persian Machine Translationmentioning

confidence: 99%

“…However, ever-expanding information exchange has changed the kind of human being's demand for translation. Now, no one expects when s/he is reading an internet page and does not comprehend some components or whole of it; a translator assists her/ him and renders the text [1]. This major change in everyone's viewpoint and rapid speed of information exchange in the modern era has resulted in thinking of developing a system, which is capable of translating the given text automatically and independently.…”

Section: Introductionmentioning

confidence: 99%

“…This major change in everyone's viewpoint and rapid speed of information exchange in the modern era has resulted in thinking of developing a system, which is capable of translating the given text automatically and independently. This system has been called machine translation [1]. Automatic machine translation was one of the first natural language processing operations analyzed in computer science and has captivated many researchers across the world for over 50 years [16].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Contrastive Analysis of Machine Translation (Google Translate) and Human Translation: Efficacy in Translating Verb Tense from English to Persian

Afshin¹,

Alaeddini²

2016

MJSS

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This study aims to examine the efficiency of Google translation in translating verb tense from English to Persian, the acceptable translation of Google translation, and the areas of possible errors in the Google translation. To this end, Google translated four passages of Oliver Twist. The Google translations were compared with the translation of Gharib based on the selected codes for the domain and extent of errors. The results were analyzed and evaluated in terms of accuracy and appropriateness. The findings of the study showed that Google Translate is not able to translate verb tense from English to Persian and the translated passages are not acceptable. Also, most of the grammatical errors occurred in translation of aspects, passives, and compounds, respectively.

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Section: The History Of English-persian Machine Translationmentioning

confidence: 99%

Section: The History Of English-persian Machine Translationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Contrastive Analysis of Machine Translation (Google Translate) and Human Translation: Efficacy in Translating Verb Tense from English to Persian

Afshin¹,

Alaeddini²

2016

MJSS

View full text Add to dashboard Cite

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“…They believed that the three systems have still long distance to human translation regarding grammatical and lexical accuracy. Ahangar, Jahangiri, and Mohammadpour (2012) in their research, "A Lexical-Functional Model for Machine Translation of English Zero-place Predicators into Persian", utilized lexical-functional grammar for a machine translation system, which was designed for the translation of some English zero-place predicators. This lexical-Functional Grammar is able to translate English zero-place predicators into Persian as one-place predicators and with consideration of Persian word order that is more natural for people whose mother tongue is Persian (p. 1).…”

Section: A Survey On the Subject-verb Agreement In Google Machine Translation 1 Introductionmentioning

confidence: 99%

A survey on the subject-verb agreement in Google Machine Translation

Bozorgian¹,

Azadmanesh

2015

IJRSET

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To investigate subject-verb agreement of Persian translated sentences in Google machine translation, 100 sentences were taken from the BBC's English web site that 50 sentences were randomly selected to be translated by both Google Machine Translator and four Human translators, to reach a reference translation. Descriptive-statistics method was used to find out; firstly, whether Google translator compared to human translator can apply subject-verb agreement properly. Secondly, whether there is a relationship between human judgment and Machine Translation automated evaluation method. To this end, after reaching a reference translation, the researcher invited four other human translators to judge and score the Google output based on three criteria. A five-point Likert Scale was used for rating the Google translated sentences. The scale (1) was assigned for a completely unacceptable translation and(5) for an excellent one with almost no errors. Then, the researcher evaluated the same 50 Google translated sentences based on three automated metrics of Precision, Recall, and F-measure. Then, the researcher described the findings through his observation of the scores given by four human translators and found out that Google translator in comparison to the human reference translation could not apply the subject-verb agreement properly in all sentences. Then, after analyzing the "human and F-measure" assessment scores by SPSS version 22 and taking the correlation coefficient of the two scores, the researcher figured out that there was a significant relationship between human and F-measure scores.

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Translating That: an Ideational Correspondence Analysis of Machine Translation

Ardianto

2021

J. Ilmu Sos. Human.

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This paper aims to contrast the translation of two machine translation systems, Google Translate and Bing Translator, in translating the lexeme in news articles. The approach used in scrutinizing the lexeme's translation correspondence in this study is systemic functional linguistics, especially in both experiential and logical structures. This study was carried out through descriptive comparative analysis. This study's data were 40 constituents that were taken from six BBC World news articles randomly selected. A thorough analysis demonstrates that the two machine translation systems can recognize the three functions of that, i.e., Head, post-modifier, and conjunction. The highest emerging function is post-modifier by 19 times (47.5%), followed by the conjunction function by 17 times (42.5%) on the first machine translation system and 18 times (45%) on the second one. The lowest emerging function is Head by four times (10%) on the first machine translation system and three times (7.5%) on the second one. Furthermore, due to the elliptical variation of that as a relative pronoun and the translation variation of that as a post-determiner, it concludes that the translation outputs of Google Translate are more accurate, semantically acceptable, creative, and contextual than those of Bing Translator.

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A Lexical-Functional Model for Machine Translation of English Zero-place Predicators into Persian

Cited by 3 publications

References 7 publications

A Contrastive Analysis of Machine Translation (Google Translate) and Human Translation: Efficacy in Translating Verb Tense from English to Persian

A Contrastive Analysis of Machine Translation (Google Translate) and Human Translation: Efficacy in Translating Verb Tense from English to Persian

A survey on the subject-verb agreement in Google Machine Translation

Translating That: an Ideational Correspondence Analysis of Machine Translation

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