Investigation of Two Types of Machines Translations Google and Targman in Five Scientific Disciplines based on BLEU Model

Ali Ashrafi
MT; NLP; BLEU; Google Translate; Targoman; IBM
In recent years, automatic translation as one of the sub-branches of natural language processing science in our country has been considered by many researchers, including the automatic translators of Targman, Faraazin, etc. In order to localize this technology, these automatic translators need to be evaluated and studied accurately and dynamically. However, large companies such as Google have also worked in this field in order to translate other languages into Persian and vice versa, but due to reasons such as inappropriate figures, calligraphy problems and other problems of Persian language in providing a good and even average translation in Persian language, Google cannot be a good machine translation for Persian language. The purpose of this study is to evaluate different translation machines including Google Translate and Targoman. For this purpose, two sentences in English and Persian in five scientific branches of linguistics, computer, psychology, genetic engineering and chemistry have been randomly selected from the scientific books of these branches. The evaluation criterion in this paper is the BLEU test, which was introduced as a standard method by IBM in 2001. After performing BLEU test on the scores obtained by each translation machine, Google Translate and Targman were ranked first to second .As the results show in a completely statistical and general way, the scores obtained by these machine translators are not satisfactory and the development of these translation machines to reach the desired level requires the efforts of researchers in this field. In addition, the goal of the current research is to examine the methods of improving machine translation using two-level sorting, linguistic features, machine translation evaluation system, semantic ambiguity, semantic similarity, structural reconstruction, as well as computerized linguistics and machine translation software. Due to the widespread increase in regional and international communications and the need for information exchange, the demand for translation has increased in recent years. They also have common and repetitive words, in which case machine translation can be used as an alternative to human translation. There are several ways to improve machine translation which this proposal deals with it.
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Received : 22 August 2022
Accepted : 30 September 2022
Published : 03 October 2022
DOI: 10.30726/ijlca/v9.i3.2022.93001