Machine Translation Technology Research Articles

Quality Evaluation of C-E Translation of Legal Texts by Mainstream Machine Translation Systems—An Example of DeepL and Metasota

Despite significant progress made in machine translation technology and the ongoing efforts in practical and commercial application of neural machine translation systems, their performance in vertical fields remains unsatisfactory. To avoid misunderstandings and excessive expectations of a specific machine translation system, this research selected legal texts as its real data research object. The text translation tasks were accomplished using two popular neural machine translation systems, DeepL and Metasota, both domestically and internationally, and evaluated using internationally recognized BLEU algorithm to reflect their Chinese-to-English translation performance in legal fields. Based on the determined BLEU score, the study adopted an artificial analysis method to analyze the grammatical aspects of the machine translation output, including the accuracy of terminology usage, word order, subject-verb agreement, sentence structure, tense, and voice to enable readers to have a rational understanding of the gap between machine translation and human translation in legal text translation, and objectively assess the application and future development prospects of machine translation in legal text fields. The experimental results indicate that machine translation systems still face challenges in achieving high-quality legal text translations and meeting practical needs, and that further post-translation editing research is needed to improve the accuracy of legal text translation.

Tulevaisuuden kääntäjän kuva

Commercial specialized translation is a rapidly evolving industry most prominently characterised by pressure towards lower prices and the implementation of new technological tools. Based on interviews of translation professionals, I describe how they expect the industry to develop: which positive and negative trends they recognize, which competence profiles translators will need, and how the implementation of machine translation tools and features will change their work. It is evident that while translation professionals are worried about the negative price development and have somewhat mixed feelings about machine translation, they believe that human translators will continue to be needed for the foreseeable future. Anticipated changes in competence profiles include increased specialization, the ability to work fast, and the ability to adopt new tools. The interviewees also expect that translators may be motivated to specialize either in post-editing of machine-translated texts, or in highly creative translations. They are well aware of the benefits and risks of machine translation; while recognizing the considerable improvement in machine translation technology, they do not believe that the typical quality problems of neural machine translation can be fully resolved.

Зміст професійної підготовки фахівців із прикладної лінгвістики в системі вищої освіти України

The article discusses professional training in applied linguistics in the higher education system of Ukraine. Much emphasis is placed on the content characteristics of such training. Its aim is to analyze and justify the content of professional training in applied linguistics in the higher education system of Ukraine. As one can see, linguistic globalization and internationalization of applied linguistics, as well as the rapid development of ICT, create the need for specialists who can design effective algorithms and methods of processing automatic text, creating automated and machine translation technologies, conducting research activities related to the skillful use of information systems. In Ukraine, linguistics is viewed as a discipline studied within the framework of philology, rather than as an independent specialty/specialization, which, in turn, significantly slows down its development and narrows its scientific potential. The specialization «Applied Linguistics» reveals the specifics of linguistics/applied linguistics, updates current demands of science and helps adjust professional training in applied linguistics to relevant standards of leading countries. A detailed analysis of corresponding university curricula shows that the content of degree programmes in applied linguistics involves mastering natural-scientific, professional-practical, humanities-related and socio-economic courses, courses chosen by each university independently, as well as optional courses. The content of degree programmes in applied linguistics is structured based on linguistic, translation-oriented, literary and combined approaches. Further research lies in studying the features of blended learning in applied linguistics in the leading countries of Europe. Keywords: linguistics, philology, applied linguistics, professional training, higher education.

Решение проблемы многоязычия в международном научно-техническом информационном пространстве

The solution to the problem of multilingualism in the international scientific and technical information space is connected with machine translation (MP) technologies. The translation process within the framework of the concept of phraseological conceptual translation of texts can be represented as the process of transmitting the semantic content of the source text by means of the output language. Within the framework of this concept, the translation of texts is provided by the conceptual analysis of the source text and the transformation of its semantic content into the target language. This approach is based on understanding the laws of the functioning of natural languages and theoretical ideas about the semantic structure of texts. The basis of the approach is the technology of automated formation of thematic dictionary bases that adequately reflect the conceptual composition of various thematic areas.

Privacy and everyday users of machine translation

Abstract The concept of privacy is central to technology use, but in machine translation (MT) research the meaning of privacy, and what it represents to everyday MT users, are both often left unquestioned. This article examines user conceptualisations of privacy and their implications for the understanding of human-MT interaction. It draws on the privacy literature, on an online forum and on a survey of MT users. The findings show that while users have concerns about sharing information such as passwords, images, and contact details with MT providers, some of them can conversely see MT as a technology that provides privacy. The article discusses these findings in relation to the informational and relational dimensions of privacy. It argues that relational privacy, and the interpersonal exchanges for which MT can be used as a substitute, should be considered more explicitly in discussions of the societal implications of MT technology.

Study on Machine Translation Teaching Model Based on Translation Parallel Corpus and Exploitation for Multimedia Asian Information Processing

Text in one language can be mechanically translated into another language using machine translation (MT). It is possible to anticipate a sequence of words, generally modeling full sentences using machine translation in a single integrated model. Human language's flexibility makes automatic translation an artificial intelligence (AI) challenge of the highest order. A single model rather than a pipeline of fine-tuned models is now the best way to attain state-of-the-art outcomes in machine translation. For example, words having numerous meanings, phrases that use more than one grammatical structure, and other grammar issues make it difficult for a machine to translate; however, many misinterpretations translate to be a breeze. A teacher's job is to assist pupils in overcoming the emotional and cognitive obstacles that stand in the way of developing effective problem-solving abilities. Students will benefit from developing problem-solving abilities since they will apply what they have learned to new circumstances. MT-AI, machine translation technology, and products have been employed in a wide range of applications, including business travel, tourism, and cross-lingual information retrieval. Text translation and phonetic translation are two types of translations that focus on the content of the source language. It is possible to create self-learning systems by injecting machine learning techniques into existing software and then observing the results of such injection. Computer software can translate a massive volume of text in a short period. It takes longer for a human translator to perform the same work as a computer program. The simulation investigation is developed based on correctness and effectiveness, demonstrating the proposed framework's reliability of 95.1%.

Cross-Context Accurate English Translation Method Based on the Machine Learning Model

The era of big data and cloud computing has come, communication between different languages is becoming more and more common, and the barriers between languages are becoming more and more prominent. As the most important means to overcome language barriers, machine translation will play an increasingly important role in modern society. The previous machine translation technology has more or less disadvantages. The accuracy of translation is too low, which is a huge bottleneck hindering the further development of machine translation technology. Therefore, based on this, we can consider modeling the cross-context accurate English translation model based on the machine translation model and rely on the working principle of machine learning. This experiment shows that the translation accuracy of our method reaches 94.2%, which is higher than 39.5% of the benchmark method. This shows that the method in this paper can reduce the influence of other factors, ensure the accuracy of cross-context English translation to a certain extent, and meet the performance improvement requirements of the English translation system.

The Use of Automated Machine Translation to Translate Figurative Language in a Clinical Setting: Analysis of a Convenience Sample of Patients Drawn From a Randomized Controlled Trial.

BackgroundPatients with limited English proficiency frequently receive substandard health care. Asynchronous telepsychiatry (ATP) has been established as a clinically valid method for psychiatric assessments. The addition of automated speech recognition (ASR) and automated machine translation (AMT) technologies to asynchronous telepsychiatry may be a viable artificial intelligence (AI)–language interpretation option.ObjectiveThis project measures the frequency and accuracy of the translation of figurative language devices (FLDs) and patient word count per minute, in a subset of psychiatric interviews from a larger trial, as an approximation to patient speech complexity and quantity in clinical encounters that require interpretation.MethodsA total of 6 patients were selected from the original trial, where they had undergone 2 assessments, once by an English-speaking psychiatrist through a Spanish-speaking human interpreter and once in Spanish by a trained mental health interviewer-researcher with AI interpretation. 3 (50%) of the 6 selected patients were interviewed via videoconferencing because of the COVID-19 pandemic. Interview transcripts were created by automated speech recognition with manual corrections for transcriptional accuracy and assessment for translational accuracy of FLDs.ResultsAI-interpreted interviews were found to have a significant increase in the use of FLDs and patient word count per minute. Both human and AI-interpreted FLDs were frequently translated inaccurately, however FLD translation may be more accurate on videoconferencing.ConclusionsAI interpretation is currently not sufficiently accurate for use in clinical settings. However, this study suggests that alternatives to human interpretation are needed to circumvent modifications to patients’ speech. While AI interpretation technologies are being further developed, using videoconferencing for human interpreting may be more accurate than in-person interpreting.Trial RegistrationClinicalTrials.gov NCT03538860; https://clinicaltrials.gov/ct2/show/NCT03538860

ГЛОБАЛІЗАЦІЯ І ПРОБЛЕМИ ЗБЕРЕЖЕННЯ МОВНОГО РОЗМАЇТТЯ

Globalization modernizes developed national languages, but accelerates the decline of «small» languages. The number of the latter is decreasing dangerously, and at an increasingly rapid pace. Оf the current 7,000 languages, the vast majority will not survive to the end of the 21st century. 150–200 languages (2–3% of the total) have a secure future. Among other things, languages fall into decay because they do not fulfil all the functions a standard language should fulfil. They are displaced from significant spheres of communication by more powerful languages. The less a language serves the socially significant spheres, the less often it is used, the faster it depreciates and decays. The traditional parameters of language vitality (number of speakers, the presence of writing and state status, standardization, areas of use, economic power) are now joined by another one — the presence of the language in the computer environment, its digital equipment. According to this criterion, languages are divided into three groups: 1) languages that have made the digital leap; 2) languages that have shown digital potential; 3) languages that have not overcome the digital threshold. Hence, their prospects in the 21st are evident. The greatest hopes for the preservation of the linguistic diversity are associated with the progress of machine translation technology. In the future, it will assume the function of an international language: enabling written and oral interaction of everyone with everyone without the need to know each other’s language. Improved machine translation is not only a convenience for speakers, but also a way to support multilingualism, a chance to preserve the numerous national languages. It can be expected that thanks to machine translation, the competition of languages for spheres of use will sharply weaken, instead their co-evolution will begin, that is, parallel, conflict-free development.

Design and Implementation of Interactive English Translation System in Internet of Things Auxiliary Information Processing

Information technology has penetrated into all aspects of human life. Nowadays, with the rapid development of science and technology, information technology has gradually become the cornerstone of the development of other technologies. The Internet of Things is an important part of the new generation of information technology. Language is the medium of communication between people. Driven by economic globalization and the development of the Internet, information is growing rapidly, and there are more and more exchanges and exchanges between countries. The emergence of high-efficiency and high-economic machine translation solves these difficulties, and the interactive English translation system is the current research hotspot, which is intended to improve the output translation quality of the English translation system. The main work of this paper is to analyze the existing interactive machine translation technology, especially the interactive machine translation based on a phrase model, using the Internet of Things as a knowledge source. According to the characteristics of segment analysis and human-computer interaction mechanism, from the network, a wealth of information are available from open sources. In this paper, on the basis of the segment analysis, the human-machine cooperation translation strategy of human-machine cooperation with complementary human-machine advantages was discussed, and the system designed was verified. It is proved that the system has high performance in improving the accuracy and recall rate of machine English translation. Compared with the existing English translation system, the accuracy has improved by more than 20% in the case of fewer iterations, and in the case of 90 iterations, the accuracy can improve by 100%.

The Importance of Machine Translation to University Students

People can no longer avoid using technology in their daily lives because it has been incorporated into every facet of life. The development of computer hardware and software to provide higher-quality translated texts is known as machine translation (MT). This research examined MT’s importance as a tool for university students using qualitative methods. Data was collected through library research from national and international journals. The results demonstrated that students typically appreciate the importance of Web-Based Machine Translation (WBMT) technology for supporting language acquisition. Only a very small number of participants felt that the availability of WBMT tools eliminated the need to learn how to write in English. This is consistent with the finding that most participants recognize the importance of cognitive involvement in English Language learning. Furthermore, MT can improve writing quality in terms of accuracy and syntactic complexity. Excessive reliance on tools may prevent students from thoroughly improving their translation skills.

Application of Convolutional Neural Network Based on Deep Learning in College English Translation Teaching Management

With the increased demand for English translators in recent years, more scholars and researchers have begun to focus on college English translation education. The demand for computers to perform automatic natural language translation is at an all-time high, presenting another potential for machine translation (MT) research. Although neural machine translation (NMT) technology has advanced significantly, it still has several flaws. The reply NN-based MT technology is not optimal in the field of long sentence translation, and there are situations such as missing translation and overtranslation. So this paper proposes college English translation based on a convolutional neural network (CNN). This paper proposes that CNNs have stronger feature extraction and information processing capabilities than other deep NNs. It successfully solves the situation of missing translation and overtranslation, lowering the quality of college English translation instruction. The experimental results of this research reveal that when the length of an English sentence grows longer, the translation accuracy based on the reply NN drops to 47.9%, while the translation accuracy based on the CNN is 89.7%. It can be seen that college English translation based on CNN can be integrated into English translation teaching as an innovation of traditional teaching methods, so as to create a learning environment for students and enhance learning effects. MT based on CNN has greatly improved students’ performance, which is conducive to improving the quality of college English translation teaching.

Overcoming Language Barriers in Academia: Machine Translation Tools and a Vision for a Multilingual Future.

abstractHaving a central scientific language remains crucial for advancing and globally sharing science. Nevertheless, maintaining one dominant language also creates barriers to accessing scientific careers and knowledge. From an interdisciplinary perspective, we describe how, when, and why to make scientific literature more readily available in multiple languages through the practice of translation. We broadly review the advantages and limitations of neural machine translation systems and propose that translation can serve as both a short- and a long-term solution for making science more resilient, accessible, globally representative, and impactful beyond the academy. We outline actions that individuals and institutions can take to support multilingual science and scientists, including structural changes that encourage and value translating scientific literature. In the long term, improvements to machine translation technologies and collective efforts to change academic norms can transform a monolingual scientific hub into a multilingual scientific network. Translations are available in the supplemental material.

기계번역의 교육적 활용 가능성 고찰 및 제안 — 把자문의 인간번역과 기계번역의 비교를 중심으로

Neural machine translation has many advantages compared to previous machine translation technologies, but it is not easy to identify which algorithms are applied to the translation process in such neural network translation, so there are still many areas that rely on human translation. This study verified the accuracy of translating Korean into Chinese through Neural machine translation, and reviewed the process with the aim of constructing error type data that can be referenced to create optimal translation requirements for Neural machine translation in the future. Therefore, this study aims to present an educational plan that can be used as a teaching tool for future Neural machine translation by analyzing in detail the problems associated with Chinese learners’ use of ‘ba(把)’ construction in previous studies and examining whether such errors appear to be same in neural network machine translation.BR Therefore, this paper first analyzed the cause of erroneous sentences that appear when using the ‘ba(把)’ construction of Korean learners pointed out in previous studies. In addition, how such errors appears in machine translation was considered. As a result of the investigation, it was found that the current Neural machine translation shows better learning ability than humans in the use of ‘ba(把)’ construction. In particular, Baidu was able to accurately grasp the environment in which ‘ba(把)’ construction was used and the environment in which ‘ba(把)’ construction was not used, and was able to accurately learn syntactic grammar data such as the location of objects, complements, adverbs, and verbs in ‘ba(把)’ construction. Therefore, based on the research results of this paper, reasonable learning methods and improvement measures were presented to Korean learners, such as the use of teaching methods using post-editing, to avoid errors occurring in the ‘ba(把)’ construction.

Babel Fish Democracy? Prospects for Addressing Democratic Language Barriers through Machine Translation and Interpretation

AbstractThis article works to show that machine translation and interpretation technologies can play significant roles in addressing language barriers in democratic polities, including beyond the state. Numerous shared‐language or interpretation models have been proposed to address such barriers, while possible machine models have largely been dismissed in the literature. Yet, online translation is now ubiquitous globally, and governments and international organizations increasingly use machine translation and some interpretation applications. Such technologies, it is shown, can greatly expand information uptake and participation by ordinary citizens in linguistically diverse polities. They also can avoid key fairness and cost concerns faced by other models, or help address them in hybrid configurations, especially in online settings. While speech‐to‐speech applications may never achieve the seamless vernacular “Babel fish” interpretations implied as necessary for some deliberative modes, machine models can be seen as valuable for addressing language barriers within a range of approaches to democracy.

Artificial Intelligence-Based Translation Technology in Translation Teaching.

With the advent of the era of artificial intelligence revolution, deep learning is undoubtedly injecting new vitality into machine translation technology. In the context of artificial intelligence, teachers must find new and effective teaching ideas to improve the actual effect of translation teaching, which has drawn the attention of university teachers in recent years. With the development of society and the change of the times, English is becoming one of the implicit requirements of society for talents. Not only in the context of the new changes in curriculum, English teaching in schools and teachers is no longer limited to examinations, but more importantly, it is also significant to cultivate students' comprehensive English ability so that they can really use their English knowledge and solve practical problems in life. On this basis, this paper investigates the practice of teaching English in college in the context of artificial intelligence.

A Snapshot of Children’s Attitudes toward Machine Translation

Technologies offered and used on the Internet play a significant part in the lives of children; nevertheless, little research has been done on how children view and use machine translation (MT). According to recent literature, there are various benefits to using MT in teaching/learning foreign languages, such as more fluent writing, more effective communication, and fewer errors. Nevertheless, the use of MT in classroom settings is often viewed as problematic by language teachers. Despite the fact that a vast number of students have used MT for various purposes or have tried experimenting with MT for certain academic or entertainment purposes, they seem to have mixed feelings about it. The present qualitative study is based on semi-structured interviews and aims to capture a snapshot of Lithuanian children’s perceptions and awareness of MT technologies. The results of the interviews reveal that children mostly find out about MT as a result of their own efforts and employ MT tools for a variety of purposes; however, at school no systematic guidance and/or support in terms of MT use is provided and children tend to perceive that their teachers generally hold negative attitudes towards MT.

An Analysis of Josa and Eomi in Translating Korean TV Dramas Into English With Artificial Intelligence

The goal of this study is to find out why the English subtitles of Korean TV dramas have frequent errors. It is anticipated that the findings would shed light on innovative ways for machine translation technology to agglutinate languages. To do this, as a first step, Korean-English subtitles were grammatically tagged according to the category part of speech (POS) to find out which POS has the most frequent errors in each language. Thirty-one groups were analyzed and categorized by tagging the part of speech. Then, for the Korean language, the Kokoma Korean morpheme analyzer was run to tag the Korean script according to the category noun, verb, adjective, etc. These were categorized into forty-five groups. This categorization included nine subsets of josa (postposition) and fourteen of eomi (ending), which are the most difficult parts of Korean to translate into English due to differences in linguistic structure. As a next step, the subtitles were scored and graded as the most corrected and the least corrected by Korean-American bilinguals. The results show that the most frequent error of josa is JX (auxiliary particle) among nine groups whereas the frequent error of eomi is EPT (tense prefinal ending).

Research on Cultural Translation Based on Neural Network

The translation of pragmatic meaning requires the processing of pragmatic implicit meaning, the translator’s responsibility for the readers of the target language to understand the pragmatic meaning, and the domestication and foreignization of cultural images. The way of pragmatic language equivalence is domestication translation to achieve the fluency of language expression. The creation of foreignized translation in order to foster the equitable interchange and dissemination of culture, which is precisely the aim of cultural translation, is the equivalence of the pragmatic meaning of cultural imagery. Neural network (NN) machine translation technology (MTT) simulates the human brain nervous system. Based on deep learning technology, the whole sentence is used as the basic unit of translation, which greatly improves the accuracy of machine translation. This paper selects the relevant texts of Shaanxi’s red tourism culture and uses the NN translation system to translate. It helps to improve the accuracy of the text translation of Shaanxi red tourism culture by analyzing the translation and providing a reference direction for further revision by neural translation users and promote the overseas dissemination of red culture. The part of the source language side that generates a vector representation of a dense and continuous sentence from the input corresponds to the encoder.

ATA2022 Annual Conference & Expo May 1–3, 2022Boston, Massachusetts

ATA2022 Annual Conference & Expo May 1–3, 2022Boston, Massachusetts