Machine-translation
V. Mirzaeian; M. Maghsoudi
Abstract
Background and Objectives: Although Machine Translation (MT) is extensively researched within the field of Artificial Intelligence (AI) and translation studies, few studies have attempted to implement MT output in foreign language teaching (FLT). One potential aspect of using MT in FLT refers to the ...
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Background and Objectives: Although Machine Translation (MT) is extensively researched within the field of Artificial Intelligence (AI) and translation studies, few studies have attempted to implement MT output in foreign language teaching (FLT). One potential aspect of using MT in FLT refers to the implementation of MT output for reading comprehension. Considering the existing gap in the body of research on this issue, the present study aimed to investigate whether MT output is qualified enough to be used as an aid in EAP reading comprehension courses. More specifically, this study intended to benchmark the efficacy of MT output for EAP reading comprehension courses based on the data obtained from testing its comprehensibility and probing the students’ perceptions. To achieve the objectives of the study, MT was operationally defined as quality assessment in terms of output efficacy, a combination of usability and comprehensibility, which mirrors the ultimate goal of MT use in EAP reading comprehension courses, from the users' or target readers’ standpoint. Within this perspective, the current research was an attempt to assess the quality of MT output in terms of comprehensibility and the degree to which MT output might be comprehensible to the EAP students participating in this study.Materials and Methods: The participants of the study, 140 Iranian undergraduate university students majoring in the field of education at Farhangian University, Iran, were selected based on simple random sampling. Oxford Quick Placement Test was used to homogenize them in terms of English proficiency. Two versions of a reliable reading comprehension test, human translation (HT) and Machine Translation (MT), were given to. This test included 25 multiple-choice items, assessing the participants' literal comprehension of information stated in the passage as well as higher-order comprehension that required making inferences and conclusions. In particular, the items measured textual coherence, inference, reference, scanning, skimming, and word-meaning inference. To test the reliability of the tests, the KR-21 formula was applied and the results showed that both HT test (.83) and MT test (.78) were reliable. To investigate the perceptions of the participants on the efficacy of the MT output they encountered on the test, semi-structured interviews were conducted with some of the participants in Persian. Findings: With reference to the results of non-parametric tests such as Spearman’s rho, and Mann-Whitney Tests, and considering the observed effect sizes (Cohen’s d), it was revealed that, generally, the efficacy of MT output is comparable to that of HT. Moreover, in terms of reading comprehension sub-skills, the qualities of the two translations were comparable with regard to scanning, and inference, but not skimming and reference. Furthermore, the findings from the interview indicated that the students perceive MT to be a seminal aid for their EAP reading comprehension activities despite the minor problems that exist in the output such as morpho-syntactic errors or inappropriate lexical equivalents.Conclusions: The present study confirmed the fact that the efficacy of MT output is target-reader-dependent and text-dependent since it is determined both by the characteristics of the readers, such as their disciplines, and text features, as demonstrated by the significant differences in comprehension levels of the same readers measured by the same questions for HT and MT output. Accordingly, this study shed limelight on comprehensibility as a criterion of MT output efficacy; that is to say, it has to be reminded that MT quality needs to be defined as a context-bound and target-reader-specific concept.
Machine-translation
V. Mirzaeian
Abstract
Background and Objectives: Although an increasing number of studies have addressed the benefits of using Machine Translation (MT) for language learning, there are other studies pointing out the disadvantages of using this technology in language learning. The general attitude in language teaching societies ...
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Background and Objectives: Although an increasing number of studies have addressed the benefits of using Machine Translation (MT) for language learning, there are other studies pointing out the disadvantages of using this technology in language learning. The general attitude in language teaching societies is the avoidance of translation – traditional form and/or electronic – for language learning. Therefore, the present study examines the application of this technology in language learning to conclude whether the use of this technology is generally useful for language learning or not.Method: One hundred articles on the impact of MT on language learning, published between 2000 and 2020, have been reviewed. To select these articles, important keywords were identified, searched in important databases and then manually by checking important journals in the field of Computer Assisted Language Learning (CALL). In this research, two methods of scientific review and meta-analysis have been used to analyze the data. In the first stage, statistical information such as number of participants, languages taught, language pairs used, variables, and language skills were extracted; and in the second stage, meta-analysis of the impact of this technology on language learning was performed. Twelve studies were selected for meta-analysis and a random model was used to follow the meta-analysis framework. Due to the small size of the samples, the G-hedge coefficient employed was based on Cohen.Findings: The results indicated that most MT studies emphasized that MT was a useful tool for language learning. However, careful examination also revealed that learners had mixed views on the use of MT for language learning. These findings raise a variety of perspectives and concerns, and while MT can increase the results of writing in a foreign language by reducing lexical errors, it may not ultimately lead to language learning. The present study identified the lack of correct educational scenarios for implementing MT in language learning. Since the mere use of tools does not guarantee learning, language teachers must first understand the nature of MT and then try to find appropriate ways to use MT with their learners. Because previous research has shown that various factors affect the quality of MT output, teachers should consider these factors. In addition, diversity among learners is itself an important issue that needs to be addressed. However, instructional design should not be limited to ways to effectively use MT in language assignments, but should look for ways to foster long-term language learning. As there is little research on this topic, more research examining the long-term effects of MT on language learning is also needed. In addition to instructional design, there are other important issues that teachers need to deal with.Conclusion: When MT becomes very accurate, instructors may encounter students' lack of motivation to learn languages. In addition, as MT becomes more prevalent, both in everyday life and in educational settings, learners may not find the use of MT immoral, and MT may become a necessity in the language learning culture in the near future. In this regard, instructors should provide students with clear guidelines on ethical considerations regarding the use of MT.
Machine-translation
M. Shamsi; M. Divani; A. Rasouli Kenari
Abstract
Background and Objectives: Sign Language is a visual language that uses hand shapes, facial expression gestures, and body language for communication. Linguistic studies have shown that Sign Languages, like spoken languages, are natural and meet the communication needs of their speakers. Sign Language ...
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Background and Objectives: Sign Language is a visual language that uses hand shapes, facial expression gestures, and body language for communication. Linguistic studies have shown that Sign Languages, like spoken languages, are natural and meet the communication needs of their speakers. Sign Language is not a single language that can be universal, nor every country has a Sign Language equivalent to its own spoken language. There are many Sign Languages in the world including the American, English, Japanese, Italian, Turkish, and Persian Sign Language. The Persian Sign Language is the natural language of the deaf in Iran. The ability to communicate effectively is an important step in establishing relationships and participation for the deaf in the community. Lack of support from the hearing community leads deaf people to isolation and other social problems. In order to overcome the obstacles that exist between hearing and hearing impaired people, a translator system is needed to translate messages into the Sign Language in accordance with their spoken language. A Sign Language translation system can be used by the deaf, the family and friends of such people, the blind-deaf people, the people who have lost their ability to speak due to some factors, the professors and teachers, the medical staff and also people who are interested in learning this language. This article, by providing an automatic translator system for translating from the Persian Language into the Persian Sign Language, aims to help the deaf in Iran to communicate better. Methods: The proposed translator system requires the use of an architecture in order to convert the Persian text into the Persian Sign Language. Due to the nature of the Persian language as the input language of the translator system and also the problems inherent in the Persian Sign Language as the output language of the translator system, the architecture of the translation systems presented in other languages cannot be presented and developed regarding the Persian language. One of the most important issues in the Persian Sign Language is lack of equivalent signs for some words in the Persian language. In order to overcome these problems, an architecture appropriate to the Persian language and the Persian Sign Language was presented. By using the proposed architecture, the proposed system, receives the Persian text as an input in the form of word or sentence from the user, and after performing the initial processing and by using the lexical transfer module, the system translates the text into the Persian Sign Language. Finally, by using an avatar, the Sign Language form corresponding the input of a word, phrase, or sentence can be represented. For this purpose, transfering the translated words into HamNoSys notation, turning the HamNoSys notation to SiGML language and then animating the 3D character with the help of this language are used. Findings: To evaluate the translation system of the Persian language to the Persian Sign Language, a set of test sentences were used. After evaluating the system, it was found that the proposed system has acceptable efficiency, storage space and speed. Conclusion: Research in interdisciplinary sciences is only effective and influential when research in all of the sciences involved is done equally and each science solves the challenges of its own field of study. For example, the most important challenge in completing the Persian Sign Language translation system is lack of linguistic research on Persian Sign Language. The proposed translation system is a combination of linguistic, social, and engineering sciences. Focusing on each of these areas and upgrading them will considerably improve the proposed system. However, the proposed system can improve the relationship between the hearing and the hearing impaired people to a great extent. One can focus on any of the proposed architecture modules and upgrade and improve each of them. It is also possible to integrate the section of emotions and facial expressions with the animated character so that the facial expressions of this avatar can change according to the conditions.
Electronic learning- virtual
V.R. Mirzaeian
Abstract
Background and Objectives: Machine translation is now widely used everywhere; However, its role as a language learning tool has not been confirmed, as there are concerns about its quality. However, if we compare the machine translation output with the output produced ten years ago, we see a significant ...
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Background and Objectives: Machine translation is now widely used everywhere; However, its role as a language learning tool has not been confirmed, as there are concerns about its quality. However, if we compare the machine translation output with the output produced ten years ago, we see a significant improvement in its quality, especially in terms of vocabulary and grammar. Machine translation can be defined as: the process by which, using electronic devices, input can be provided from one language and output delivered in another language. When machine translation became available on smartphones, it gained universal acceptance because of its benefits such as free and easy access. In the field of education, many learners use this technology every day for various personal as well as academic purposes. These goals mainly include understanding a text that is not written in the native language or translating different texts from different languages into other languages and delivering it as homework. Machine translation can help learners gain a quick understanding of a text written in a language other than their mother tongue by producing an incomplete version. The aim of this research was to assess the quality of machine translation and its impact on students’ reading comprehension. Methods: Three types of texts were selected with varying levels of difficulty. These texts were translated once by a human translator and once by machine translation (Google Translator). Finally, six texts were obtained. The output of machine translation was evaluated and analyzed. Postgraduate students who happened to use machine translation more frequently were then randomly divided into six groups, each group reading one of these texts and answering multiple choice comprehension questions at the end of the text. The T-test was performed on the data and it was found that from the three types of texts, the two types of texts, despite having some lexical and grammatical problems, were able to compete with human translation. Findings: The data showed that the quality of machine translation is improving and has now reached a degree of quality that can be used as a tool in educational environments. Some guidelines were also given on how to use this technology in the classroom. Conclusion: This study attracts attention of language educators to MT and its use in language teaching. It suggests that language educators should be trained to use this tool to improve language learning among students. Considering that the type of text has a great impact on the quality of machine translation and very good scientific texts and very bad literary texts are machine translated, this point should also be considered in generalizing the results of this research. All three texts translated by Google were able to match the human translated text in terms of comprehension, but the number of unknown sentences in this text was more than the other two texts, which were expected to have a negative effect on students' comprehension, which was not observed. The issue of gender can also be examined to see if there is a relationship between gender and the type of reaction to machine translation or not.
