Machine Translation

Many studies abroad have focused on neural machine translation and almost all concluded that this method was much closer to humanistic translation than machine translation. Therefore, this paper aimed at investigating whether neural machine translation was more acceptable in English-Persian translation in comparison with machine translation. Hence, two types of text were chosen to be translated by Google Translate. The inputs have been translated in two distinctive methods. The outputs were investigated by the descriptive-comparative human analysis model of Keshavarz. Consequently, the results revealed that approximately the same errors were found in both methods. However, semantic aspects were improved.

This study aimed at investigating the Persian human and machine (Google) renderings of The Kite Runner by Khalid Hosseini, through comparing and contrasting culture specific items (CSI) as well as the exploited translation strategies. Thus, the relevant problematic forms of the cultural differences were identified and the procedures suggested by Newmark (1988) were examined to see how they fit into the translation of cultural differences in English and Persian. The results of this qualitative study revealed that Literal Translation, Naturalization and Transposition strategies were used most frequently in both human and machine translations. However, machine translation could not present a comprehensible translation due to overuse of these strategies (75%). It was also revealed that the spirit of source text was kept intact in both renderings due to closeness of Iranian and Afghan cultures. It was further discovered that the human translated version carried the real beauty and creativities of the original work. In facty, the terms that were transformed and localized were able to carry over the flavor of the author’s work. Finally, it was shown that the regretful theme of the source text was reserved to a great extent in the human rendering of the novel, while machine translation failed to get it. Generally-speaking, based on the findings of this study, culture-specific terms make it difficult for machines to achieve complete word-for-word equivalence, and at the same time, human translator must have a broad knowledge of the literature and traditions of both the source and target languages to come up with a faithful translation in terms of both form and content.

The present descriptive study aimed at investigating the human and machine Persian translations of The Kite Runner by Khalid Hosseini, and comparing the applied translation strategies in the translated texts for culture-specific items (CSI). To this end, based on Newmark’s (1988) category, the applied strategies were identified in the two translations and compared. The obtained results showed that Naturalization and Transposition strategies were the most frequently-used strategies by both human translators and machine translation. The results also showed that machine translation could not present a comprehensible translation due to overuse of these strategies (75%). It was further revealed that the spirit of the original text was not lost in the translated versions due to the closeness of Iranian and Afghan cultures. In fact, the translated versions kept the real beauty and creativity of the original work. However, the remorseful theme of the source text was kept intact to a great extent in the human translation of the novel, while machine translation lost it. Thus, the general impression is that culture-specific terms make it difficult for the machine translation to achieve complete word-for-word and semantic equivalence, and that the human translator must have a broad knowledge of the literature and traditions of both the source and target languages.

Linguistic and semantic differences are some of the main problems of translating the Holy Qur’an into English. The present study highlights the problem of lexical gap and examines a number of terms- totally 117 in 110 verses- of the Holy Qur’an, including the referential meaning of ‘sin’ and their English translations. The researcher aimed to find the strategies applied by three translators and three machine translation systems (MSTs) and to compare them.  In this regard, five frequent and common terms – ‘اثم’, ‘جناح’, ‘سیئه’, ‘ذنب’, and ‘وزر’-were selected. The strategies proposed by Mollanazar (2009) were employed to fill the gap.  To do so, the English translations produced by three machine translation systems (MTSs), namely Google Translate, SDL Free Translation and Systranet were compared with three human translation by M.H. Shakir, A.Qaraei and T.B.Irving. The results revealed that in most verses, almost in six English translations, a generic term was used without any additional information to make the sense clearer. There was no noticeable difference between human and machine translations in applying the proposed strategies to fill the gap and make the English version more meaningful in terms of these apparently similar but contextually different terms. Thus, it seems that these differences were not focused on, while rendering these given verses to English. 

Google Translate , a free multilingual machine translation service, developed by Google has attracted the attention of countless users due to its ease of use through modern means of mass communication, and has become the only translation tool in some areas. However, compared to human translation, these machine tools have not yet been able to deliver high-quality translations due to the complexity of translation process. Therefore, studying the modifications of machine translated texts is of great importance. Therefore, the current study aimed to explore the types of linguistic modifications of the texts translated from Persian into English through Google Translate. To this end, the abstracts of ten unpublished Persian literary articles intended to be submitted to Iranian journals were selected for the analysis. The selected abstracts were initially translated into English (target language) through Google Tra n slate from Persian (source language). To identify the kinds of changes needed to make them academically acceptable, the machine translated texts were all post edited. Then, the original Google translated texts and their post edited versions were compared to figure out the types of the applied modifications. The results of this qualitative study indicated that the linguistic post edition modification of the texts included tense, literal translation, redundancy, collocations, deletion of the main verb, word-choice and proper nouns.

This quantitative study aimed to investigate Iranian EFL student teachers’ perceptions on the use of Machine Translation (MT) for foreign language learning in academic context. To this end, 107 EFL student teachers from a women-only state university in Tehran, Iran, completed a recently developed and validated questionnaire in the field. The findings revealed that most participants were familiar with digital technology including MT and its different types such as Google Translate (GT). Satisfied with MT output, the majority of the participants in the study installed MT apps on their smartphones or used its website on their computers to complete assignments or to translate from Persian to English and vice versa. However, they were neutral about whether their instructors confirmed their MT use, or whether they preferred their teachers know they use MT or not. They were also not sure whether consulting MT was against the regulations. The results showed that authorities in the field of foreign language teaching are required to take a positive stand on this emerging technology; in addition, considering the importance of training for both instructors and learners, they should hold workshops for more responsible and effective MT implementation. 

The world of foreign language education has been immensely influenced by the glory of emergent machine translation (MT) technologies including Google Translate (GT) (Knowles, 2022). Considering that end users' perceptions reflect GT practicality, ample research has been conducted regarding language learners’ perceptions on GT use. Yet, investigating Iranian student teachers' perceptions on the use of GT as an ICALL tool for language learning in higher education has been underestimated. To bridge this gap, semi-structured interviews with twelve student teachers, who were selected through purposive convenience sampling, were conducted employing qualitative constructivist grounded theory methodology. Data were analyzed based on the grounded theory data coding principles (open, axial, and selective) using the MAXQDA 2020 software. A model of GT use in language learning, entitled ‘Google Translate-Assisted Language Learning (GTALL) was proposed. The three main categories (i.e. GT familiarity and use, Perceptions, and legitimacy) along with 35 sub-categories at two levels supported our core category ‘implementation of GT in language learning’. The results demonstrated considerable pedagogical implications for educational stakeholders. For administrators, to appreciate contemporary pedagogical transformations to fulfill new generation’s needs. For professors, to improve digital literacy, welcome emergent technologies, and bring them into their learners’ service for greater educational achievements, and for language learners, to develop technological skills that guarantee wise and efficient human-machine interactions.

Thanks to the significant achievements in Artificial Intelligence (AI), Machine Translation (MT), in general, and Google Translate (GT), in particular, have been extensively used in all facets of life, including language learning. However, faced with a plethora of research evidence on GT’s educational contributions, erroneous translations create disparity regarding its use in language learning. To address this lacuna, the present study systematically reviewed 10 databases, namely, Web of Science, Scopus, ERIC, ScienceDirect, Taylor & Francis Online, Wiley Online Library, SAGE Journals, Springer Link, Springer Open, and DOAJ. Additionally, it hand searched the reference lists of 44 studies selected to be included in the synthesis from database search along with references cited in three previous systematic reviews on similar topics to capture a comprehensive view of the literature related to the use of GT in language learning between 2010-2021. It reviewed 50 studies witnessing a rise in the number of studies in this area. Studies reported that although significant improvements in the quality of GT led to pedagogical gains and more tendency to implement it in language learning, instructors still distrust it. Accordingly, this research provides pedagogical implications and suggests avenues for future research on the use of GT in language learning.     

Crowdsourcing Translation as a Post-Editing Method (CTPE) has emerged as a rapid and inexpensive method for translation and has drawn significant attention in recent years. This qualitative study aims to analyze and synthesize the approaches and aspects underpinning CTPE research and to identify its potential that is yet to be discovered. Through a systematic literature review focused on empirical papers, we examined the limited literature thematically and identified recurring central themes. Our review reveals that the topic of CTPE requires further attention and that its potential benefits are yet to be fully discovered. We discuss the eight core concepts that emerged during our analysis, including the purpose of CTPE, CTPE areas of application, ongoing CTPE processes, platform and crowd characteristics, motivation, CTPE domains, and future perspectives. By highlighting the strengths of CTPE, we conclude that it has the potential to be a highly effective translation method in various domains.