Online Machine Translation Systems Research Articles

Exploring the Role of Machine Translation in Translating English Collocations into Arabic: Insights from Student Translators

Machine Translation (MT) has increasingly become an essential technology in the modern age. MT technology is currently used by many EFL learners as a learning facilitator. They are using MT as an essential tool to assist them in their foreign language learning activities. Several studies have focused on investigating the EFL students’ use of and attitudes towards MT in various EFL learning activities including reading, writing and vocabulary acquisition. However, few studies have been conducted on exploring the EFL learners’ use of MT technology in the translation of collocations, especially in the Arabic context. This study addresses this gap by investigating the impact of MT on the translation of English lexical collocations into Arabic. It presents a corpus of twenty English collocations given to thirty third-year translation students at an Omani university, who utilized an online MT system for their translations. Employing a descriptive, qualitative approach, the study assesses students' strategies and the accuracy of MT-generated equivalents, drawing from translation models by Vinay and Darbelnet (1958) and Newmark (1988). The results indicated that the students were able to generate correct translations for certain collocations when using MT, but there were inaccuracies in the translation of other collocations. The study emphasized the importance of not solely depending on MT because doing so might reduce students' willingness to actively search for the most appropriate translations on their own. This suggests that a balanced approach to using MT and encouraging students to develop their translation skills independently is advisable. Future research can explore the use of Machine Translation in translating collocations in languages beyond Arabic and within different cultural and linguistic contexts.

Translation Evaluation of Three Machine Translation Systems, with Special References to Idiomatic Expressions

One of the major challenges posed by machine translation (MT) when translating between English and Arabic is idiomatic expressions since their meanings are not taken literally but have a cultural meaning behind them, and they are “all in all metaphorical and cannot be understood directly” (Ghazala, 2003). Therefore, the main aim of the present study is to evaluate and compare the performance of three online MT systems namely, Google, Microsoft Bing, and Systran in translating English idiomatic expressions into Arabic in online newspapers. To achieve the goals of the present study, twenty idiomatic expressions which are the most frequently used in six British and American online newspapers have been identified, fed, discussed, compared and analyzed qualitatively by the researchers. Findings of the present study show that although the three online MT systems were not able to provide accurate translations of the English idiomatic expressions into Arabic in six online newspapers, Microsoft Bing machine is rated the best and has a better performance. Whereas Googles’s translation is found the least accurate as some words were not translated at all, and others were mistranslated. The study concludes that MT systems can be improved to get a better quality and provide an accurate translation if they deal with the idiomatic expressions as a unit and not individually. Since these are fixed idiomatic expressions, they are usually resolved using a simple memory translation, which sets side-by-side the English and the Arabic expressions without much cognitive effort.

Post-editing for the kazakh language using opennmt

The modern world and our immediate future depend on applied intelligent systems, as new technologies develop every day. One of the tasks of intelligent systems is machine (automated) translation from one natural language to another. Machine translation (MT) allows people to communicate regardless of language differences, as it removes the language barrier and opens up new languages for communication. Machine translation is a new technology, a special step in human development. This type of translation can help when you need to quickly understand what your interlocutor wrote or said in a letter. The work of online translators used to translate into Kazakh and vice versa. Translation errors are identified, general advantages and disadvantages of online machine translation systems in Kazakh are given. A model for the development of a post-editing machine translation system for the Kazakh language is presented. OpenNMT (Open Neural Machine Translation) is an open source system for neural machine translation and neural sequence training. To learn languages in OpenNMT, you need parallel corpuses for language pairs. The advantage of OpenNMT is that it can be applied to all languages and can handle large corpora. Experimental data were obtained for the English-Kazakh language pair. Experimental data were obtained for the English-Kazakh language pair.

Эффективность систем машинного перевода в урбанистическом дискурсе

This article aims to analyze the use of Yandex.Translate, an online machine translation system, in translating urban discourse texts on the web. The authors use integrative linguistic-and-pragmatic approach to assess machine translation quality in a global digital setting. The aim is to show the efficiency of a state-of-the-art machine translation system and to investigate its usefulness in practical application. The authors perform a detailed analysis of the Paris city website content, which is automatically translated from French into Russian with Yandex.Translate. The data selection is justified by the absence of official foreign versions of this website, which points to the need of machine translation engines integrated in a web browser. Less than 20% of the analysed machine-translated texts demonstrate high language quality, whereas 60% can be referred to as acceptable – the text preserves the meaning of the source but contains some errors and inaccuracies in the target language. About 20% of the machine-translated text contains blunders, which violate Russian language norms. It causes source text contents distortion and communication failures. In the end, a classification of the system errors is presented. It is also concluded that machine translation would substitute middle-skilled human translators in the future. However, the use of such systems will enforce standardisation and simplification of the target language.

Teaching Arabic Machine Translation to EFL Student Translators: A Case Study of Omani Translation Undergraduates

The present paper describes a machine translation (MT) course taught to undergraduate students in the Department of English Language and Literature at Dhofar University in Oman. The course is one of the major requirements for BA in Translation. Fifteen EFL translation students who were in their third year of study were enrolled in the course. The author presents both the theoretical and practical parts of the course. In the theoretical part, the topics covered in the course are outlined. As for the practical part, it focuses on the translation students’ post-editing of online MT output. This is beneficial to the students as free online MT systems can potentially be used as a means for improving student translators’ training and EFL learning. This is achieved through subjecting MT output to analysis or post-editing by the students so that they can focus on the differences between the source and target languages. With this goal in mind, assignments were given to the students to post-edit the Arabic and English MT output of three free online MT systems (Systran, Babylon and Google Translate), discuss the linguistic problems that they spot for each system and choose the one that has the fewest number of errors. The results show that the students, with varying degrees of success, have managed to identify some linguistic errors with the MT output for each MT system and thus produced a better human translation. The paper concludes that there is a need to incorporate MT courses in translation departments in the Arab world, as integrating technology into translation curricula will have great effect on student translators’ training for their future career as professional translators.

The Varibility of Reproduction: Emotive Units in a Literary Text (On the material of Ukrainian, Russian and Chinese) Variation of reflection of English-speaking emotional units in the translation of an artistic work (in Ukrainian, Russian, Chinese)

The article is focused on peculiarities of English and Chinese political discourse translation into Ukrainian. The advantages and disadvantages of machine translation are described on the basis of linguistic analysis of online Google Translate and M-Translate systems. The reasons of errors in translation are identified and the need of post-correction to improve the quality of translation is wanted. Key words: political discourse, automatic translation, online machine translation systems, machine translation quality assessment.

Metadata records machine translation combining multi‐engine outputs with limited parallel data

One way to facilitate Multilingual Information Access (MLIA) for digital libraries is to generate multilingual metadata records by applying Machine Translation (MT) techniques. Current online MT services are available and affordable, but are not always effective for creating multilingual metadata records. In this study, we implemented 3 different MT strategies and evaluated their performance when translating English metadata records to Chinese and Spanish. These strategies included combining MT results from 3 online MT systems (Google, Bing, and Yahoo!) with and without additional linguistic resources, such as manually‐generated parallel corpora, and metadata records in the two target languages obtained from international partners. The open‐source statistical MT platform Moses was applied to design and implement the three translation strategies. Human evaluation of the MT results using adequacy and fluency demonstrated that two of the strategies produced higher quality translations than individual online MT systems for both languages. Especially, adding small, manually‐generated parallel corpora of metadata records significantly improved translation performance. Our study suggested an effective and efficient MT approach for providing multilingual services for digital collections.

Evaluating Urdu to Arabic Machine Translation Tools

Machine translation is an active research domain in fields of artificial intelligence. The relevant literature presents a number of machine translation approaches for the translation of different languages. Urdu is the national language of Pakistan while Arabic is a major language in almost 20 different countries of the world comprising almost 450 million people. To the best of our knowledge, there is no published research work presenting any method on machine translation from Urdu to Arabic, however, some online machine translation systems like Google , Bing and Babylon provide Urdu to Arabic machine translation facility. In this paper, we compare the performance of online machine translation systems. The input in Urdu language is translated by the systems and the output in Arabic is compared with the ground truth data of Arabic reference sentences. The comparative analysis evaluates the systems by three performance evaluation measures: BLEU (BiLingual Evaluation Understudy), METEOR (Metric for Evaluation of Translation with Explicit ORdering) and NIST (National Institute of Standard and Technology) with the help of a standard corpus. The results show that Google translator is far better than Bing and Babylon translators. It outperforms, on the average, Babylon by 28.55% and Bing by 15.74%.

Improved Named Entity Recognition using Machine Translation-based Cross-lingual Information

In this paper, we describe a technique to improve named entity recognition in a resource-poor language (Hindi) by using cross-lingual information. We use an on-line machine translation system and a separate word alignment phase to find the projection of each Hindi word into the translated English sentence. We estimate the cross-lingual features using an English named entity recognizer and the alignment information. We use these cross-lingual features in a support vector machine-based classifier. The use of cross-lingual features improves F 1 score by 2.1 points absolute (2.9% relative) over a good-performing baseline model.

Evaluating Arabic to English Machine Translation

Online text machine translation systems are widely used throughout the world freely. Most of these systems use statistical machine translation (SMT) that is based on a corpus full with translation examples to learn from them how to translate correctly. Online text machine translation systems differ widely in their effectiveness, and therefore we have to fairly evaluate their effectiveness. Generally the manual (human) evaluation of machine translation (MT) systems is better than the automatic evaluation, but it is not feasible to be used. The distance or similarity of MT candidate output to a set of reference translations are used by many MT evaluation approaches. This study presents a comparison of effectiveness of two free online machine translation systems (Google Translate and Babylon machine translation system) to translate Arabic to English. There are many automatic methods used to evaluate different machine translators, one of these methods; Bilingual Evaluation Understudy (BLEU) method. BLEU is used to evaluate translation quality of two free online machine translation systems under consideration. A corpus consists of more than 1000 Arabic sentences with two reference English translations for each Arabic sentence is used in this study. This corpus of Arabic sentences and their English translations consists of 4169 Arabic words, where the number of unique Arabic words is 2539. This corpus is released online to be used by researchers. These Arabic sentences are distributed among four basic sentence functions (declarative, interrogative, exclamatory, and imperative). The experimental results show that Google machine translation system is better than Babylon machine translation system in terms of precision of translation from Arabic to English.

Refinements of machine translation by automatic editorial operations

The performance of state-of-the-art machine translation (MT) systems is still far from being perfect. In this article, we investigate the combination of on-line MT systems to produce better outputs. The basic idea is to apply editorial operations of substitution, insertion, and deletion to candidate outputs in an automatic fashion. The proposed methods are evaluated on the translation task in the travel domain from Chinese to English as defined in the International Workshop on Spoken Language Translation evaluation campaign. Outputs of three on-line MT engines are combined. The proposed method is evaluated on Chinese-to-English MT tasks in the tourism domain. An overall improvement of 1.4 in the BLEU score over the best single-system baseline has been achieved.

Information Technologies and Translation Instrumental Competence

This paper outlines the types of information technologies applied to translation today and translation sub-competences. A study was made in which students were motivated to participate in building DIY corpus based on a given task and to solve the problems in their former translation. It was found that students instrumental competence was improved in recognizing, evaluating, and utilizing information technologies in solving real translation problems, and that their other translation sub-competences could be acquired when translating a real world task in highly contextualized teaching environment. The purpose of the study is to showcase the necessities of including the revising skills required for online machine translate into instrumental competence of language learners at undergraduate level and the possibilities and limitations of online machine translation system when translating Chinese texts into English.

Evaluation of Online Machine Translation by Nursing Users

Nurses whose native language is not English may face a language barrier when they read nursing literature published internationally. Freely accessible online machine translation could be one possible solution to reduce their burden. In the present study, English-Japanese translations of nursing article abstracts from the major online machine translation system, Google Translate, were evaluated by 250 nursing faculty members across Japan using a previously verified method for evaluating intelligibility. The abstracts were evaluated according to sections typically classified in structured abstracts: title, background, aim, method, result, discussion, conclusion, and relevance to clinical practice. We also asked for respondents' impressions on the perceived usefulness of the translated abstract as a whole. The results indicated that Google Translate had minimally acceptable quality and identified the word count and the existence of unidentifiable words of source texts as factors contributing to translation quality. Nurses may benefit from Google Translate when reading the professional literature. However, our results suggest that more effort should be made to improve online machine translation performance, and emphasize the importance of providing training and education for nursing professionals to utilize this technology.

Online Machine Translation Use With Nursing Literature

Japanese nurses are now being required to use research in clinical practice. As a result, they increasingly need to read nursing research literature written in English. Online machine translation is a convenient tool that may address any existing language barrier. The quality of machine translation has been evaluated using various methods; however, its reliability for nursing literature is unknown. In this study, existing methods for evaluating online machine translation quality were examined for structural accuracy and intelligibility of translations of nursing literature, and the usability of machine translations is discussed. In total, 297 English sentences from nursing study abstracts were evaluated for accuracy and intelligibility; 75 word items were identified as specific terms. Interrater reliability of the evaluation method was measured using two raters. The results showed the strong reliability of the existing evaluation method. Also, a possibly usable online machine translation system being offered in Japan was identified. Specific terms seemed to influence the extent of the intelligibility of the translations. Future studies need to include more samples of the nursing population to examine general perceptions of the usability of online machine translation systems for reading English nursing literature among the Japanese nursing population.

Preliminary study of online machine translation use of nursing literature: quality evaluation and perceived usability

BackgroundJapanese nurses are increasingly required to read published international research in clinical, educational, and research settings. Language barriers are a significant obstacle, and online machine translation (MT) is a tool that can be used to address this issue. We examined the quality of Google Translate® (English to Japanese and Korean to Japanese), which is a representative online MT, using a previously verified evaluation method. We also examined the perceived usability and current use of online MT among Japanese nurses.FindingsRandomly selected nursing abstracts were translated and then evaluated for intelligibility and usability by 28 participants, including assistants and research associates from nursing universities throughout Japan. They answered a questionnaire about their online MT use. From simple comparison of mean scores between two language pairs, translation quality was significantly better, with respect to both intelligibility and usability, for Korean-Japanese than for English-Japanese. Most respondents perceived a language barrier. Online MT had been used by 61% of the respondents and was perceived as not useful enough.ConclusionNursing articles translated from Korean into Japanese by an online MT system could be read at an acceptable level of comprehension, but the same could not be said for English-Japanese translations. Respondents with experience using online MT used it largely to grasp the overall meanings of the original text. Enrichment in technical terms appeared to be the key to better usability. Users will be better able to use MT outputs if they improve their foreign language proficiency as much as possible. Further research is being conducted with a larger sample size and detailed analysis.