Application of Translation Technology in AIpowered Translation Workshop

Abstract

With the growth of the language service industry and the influence of artificial intelligence on translation, it is crucial to integrate more translation technologies into translation teaching. This paper explores how translation technologies and Gen-AI tools are utilized in translation workshops in the AI era. In order to solve the existing problems in current translation teaching, this paper designed a teaching model for translation workshops, utilizing an artificial intelligence teaching platform to concentrate on teaching materials, methods and approaches, translation practice frequency, and the AI-bascd assessment of works. This study provides new ideas for cultivating translation talent in the AI era by incorporating machine translation, large language model translation, terminology management, and corpus technology into teaching; adopting flipped classrooms, blended learning, and project-based teaching approaches; and constructing a comprehensive evaluation system.

Headnote

Abstract

With the growth of the language service industry and the influence of artificial intelligence on translation, it is crucial to integrate more translation technologies into translation teaching. This paper explores how translation technologies and Gen-AI tools are utilized in translation workshops in the AI era. In order to solve the existing problems in current translation teaching, this paper designed a teaching model for translation workshops, utilizing an artificial intelligence teaching platform to concentrate on teaching materials, methods and approaches, translation practice frequency, and the AI-bascd assessment of works. This study provides new ideas for cultivating translation talent in the AI era by incorporating machine translation, large language model translation, terminology management, and corpus technology into teaching; adopting flipped classrooms, blended learning, and project-based teaching approaches; and constructing a comprehensive evaluation system.

Keywords

Artificial Intelligence; Translation Technology; Translation Workshop; Translation Teaching

1. Introduction

Due to the rapid advancement of artificial intelligence (AI) technologies and increased globalization of markets, the language service industry has experienced rapid transformations. There's been a significant increase in translation services, heavily influenced by innovative technologies like Neural Machine Translation (NMT), terminology management systems, and other AI-powcrcd translation tools. These technological advancements are reshaping the translation landscape and posing new challenges for professional translators and translation teachers in the field. There is a widespread perception nowadays among both translation students and trainers that familiarity with translation technology is of paramount importance to embark on a successful professional career (Gaspari & Doherty, 2015). This phenomenon is significantly influenced by the integration of AI technologies into translation workflows, requiring translators to develop new technical skills.

In response to these changes, teachers are concentrating on how to incorporate AI technologies and translation tools into teaching practices effectively. Traditional translator education is limited to foreign language skills such as listening, reading and writing, and translation skills, and the field of knowledge is confined to literature, culture, and language. The traditional training mode is accordingly more focused on students' language proficiency and translation skills, and there are few courses related to translation technology (Gao & Zhang, 2024). There is a consensus that the future of translation education lies in the integration of translation technology into translation practice, aiming to ready our students for the growing industry and to deepen a comprehensive understanding of how AI tools can be used for better translation quality and efficiency. The training of translation talents no longer sets cross-cultural communication abilities and basic translation theories as the primary objectives, but the ability to apply information technology to solve practical problems, cooperation awareness, innovative minds, management power, and critical thinking (Wang & Xie, 2023).

A major change in the translation industry has revolutionized translation training, emphasizing the cultivation of "composite" translators and interpreters who demonstrate linguistic adeptness as well as proficiency in utilizing translation techniques (Zhong, 2021). Consequently, many universities and translation programs are exploring creative ways to incorporate AI and other translation technologies into their courses through translation workshops and project-oriented learning.

1.1 Research Objectives

The aim of this paper is to explore the effective application of translation technologies in AI-powered translation workshops and to introduce a new teaching model that incorporates these technologies into translation practice. It investigates how translation technologies can be effectively incorporated into translation workshops, including machine translations, large language models (LLMs), terminology management systems, and corpus tools. It develops a teaching model that addresses existing challenges in translation education, including the lack of practical training, limited student engagement, and outdated teaching approaches. Besides, this paper proposes a comprehensive assessment framework that evaluates students' linguistic competence and their ability to utilize translation tools.

1.2 Structure of the Paper

In this paper, chapter two provides a concise review of the literature on translation technology and its role in teaching translation, combining the development of AI tools and their integration into translation education. Chapter three discusses an AI-powered translation workshop model, explaining the selection of teaching materials, methods, and practice frequency. Chapter five outlines a comprehensive assessment system that evaluates translation quality, technical skills, and project management competencies. The last chapter lists the limitations of this study, its implications, and suggestions for future research in this field.

2. Literature Review

2.1 The Development of Translation Technologies

The fast development of translation technology, especially due to the emergence of AI and NMT, has drastically transformed the translation industry. In recent years, end-to-end NMT has achieved great success and has become the new mainstream method in practical machine translation (MT) systems (Tan et al., 2020). In practice, NMT also become the key technology behind many commercial MT systems (Hassan et al., 2018). NMT systems, such as Google Translate and DeepL, depend on deep learning algorithms to generate translations that are more fluent and contextually precise (Wu, 2016). However, MT still faces challenges, requiring human post-editing to guarantee its quality.

The integration of translation technology into professional translation workflows has resulted in the development of tools such as computer-assisted translation (CAT) systems, which combine human skills with machine memories. When translating a document, translators typically gather and consult corpora of parallel texts (texts that have the same communicative function as the source text but have been written independently in the target language) for guidance with regard to appropriate style, format, terminology, and phraseology (Bowker, 2002). Nearly 80% of language service providers have some MT or CAT tools in their working environment to boost their productivity. The rising dependence on AIdriven technologies in translation highlights the necessity for translators to master these tools effectively.

In addition to NMT and CAT tools, terminology management and corpus-based translation tools have also become essential in ensuring translation consistency and accuracy. Maintaining consistency and precision in highly specialized translation projects, like legal, financial, medical, and manufacturing, is only possible with a glossary of terms that provides the term, its definition, and the most precise translation. And Translation-driven corpora: Corpus resources for descriptive and applied translation studies (Zanettin, 2012).

2.2 The Application of AI in Translation

The rise of generative AI (Gen-AI) and LLMs has further enhanced better quality in translation. It enhances efficiency and standards in the field of language translation, facilitating worldwide contact and highlighting the escalating need for inventive technical remedies that could address the longstanding challenge posed by language barriers or restrictions (Mohamed et ah, 2024). Current LLMs have shown potential in understanding complex sentence structures, idiomatic expressions, and cultural backgrounds, which are often not properly translated by traditional NMT systems.

However, integrating LLMs into translation workflows still faces some challenges. Applying ChatGPT to translation teaching poses certain ethical risks, including data leakage risks, bias issues, and academic integrity issues (Peiran Fan et al., 2023). The application of LLMs in translation still requires careful human oversight, especially in professional texts with a higher level of translation accuracy. This highlights the importance of training translators and interpreters to work with AI tools, ensuring that they can effectively apply these technologies while maintaining quality control.

2.3 Translation Workshop in the AI Environment

The concept of the translation workshop has been fundamental to the field of translation teaching. Foreign language learning alone does not constitute adequate linguistic preparation for professional translators; they must also have acquired the languages they work with, and that acquisition can indeed be fostered in the classroom (Kiraly, 2000). The translation workshop can give students opportunities to use the translation theories they've learned in practice, as professional translators. Learners are often equipped with the Internet environment and one or two CAT tools. So, instead of translating on paper with a pen and dictionaries, they translate with technology. However, the integration of AI tools into translation workshops brings new working modes with chances and challenges. These advanced technologies have the potential to enhance students' translation efficiency, allowing them to focus more on linguistic and cultural aspects.

The use of AI in translation workshops can help bridge the gap between academic learning and language service industry practices. By incorporating advanced AI tools into properly selected project-oriented tasks, students can experience the tools and workflows they will encounter in their future careers. If a high-quality translation environment can be created with the help of AI technology, it is to create favorable conditions for our university students to improve their English translation skills. This will ensure greater achievements in English subject education in our higher education institutions, thus ensuring that students can achieve all-round development and improve their overall quality while mastering translation skills (Kong, 2022). However, there still are challenges to properly integrating AI tools into translation workshops with social norms and ethics. Some scholars highlight the difficulty of balancing traditional translation skills with the demands of using AI tools. Some students might face difficulties in the challenging learning process of mastering these tools, potentially diverting their focus on improving basic translation competencies. Therefore, educators must rethink the teaching mode of translation workshops to ensure that students are not overwhelmed by the translation technologies.

2.4 Challenges in Traditional Translation Teaching Methods

Traditional translation teaching emphasizes more on theory over practice. In many universities, translation classes still focus heavily on translation theories and textual analysis, lacking awareness of practical skills and the use of translation technologies. However, the teaching of translation in colleges and universities is mostly limited to teaching translation skills and translation theories in the classroom, and the main teaching contents arc literary works and social science materials (Li, Lv, & Zhang, 2019). This broadens the gap between the study in schools and the demands of the modern language service industry.

Furthermore, the lack of translation training in many translation workshops worsens the problem. Students often graduate with little or no experience using translation technologies, resulting in a series of training courses provided by companies when they enter the workforce. The fact is that many translation teachers themselves are not proficient in the latest translation technologies, limiting their ability to teach these tools effectively.

Besides, some students prefer to use AI tools without any critical thinking and checking logic. However, some students are reluctant to use translation technologies, believing them to be complicated and not essential for their future careers. This reluctance is often due to a lack of understanding of how these tools can enhance translation quality and efficiency. In order to solve this, teachers must find ways to encourage students by demonstrating the practical benefits of using AI tools in translation work and designing more practices involving the use of AI tools. Integrating technology into translation workshops in a structured and guided method can help alleviate these concerns and encourage students to embrace new tools.

Since technology literacy and technology competence has been introduced into the category of translator competence (Wang & Qin, 2018; Zhu & Guan, 2019), technology is continuously shaping the future ways and methods of teaching translation. Properly assisted by AI tools in translation workshops, it offers a new approach to narrowing the gap between theory and practice. However, teachers must keep learning translation-related technologies, and then balance the teaching of basic translation competencies and the technical demands. Ultimately, the effective combination of AI technologies and translation education will depend on the deployment of creative teaching models that equip students with essential technical skills and cultivate a deep understanding of the translation process.

3. AI-powered Translation Workshop Teaching

Translation workshop offers students the opportunity to apply theories in practice. However, the appearance of AI technologies, MT, and CAT tools has already exerted an influence on the teaching model of translation workshops. An AIpowcred translation workshop teaching will enhance students' engagement and enthusiasm from the aspects of teaching materials, teaching methods and approaches, and practice frequency. The AI-powercd translation workshop teaching incorporates advanced tools such as MT, LLMs, and terminology management systems, and employs innovative teaching approaches like flipped classrooms, blended learning, and project-oriented methods.

3.1 Teaching Materials and Resources

One of the most critical aspects of teaching is the selection of appropriate teaching materials and resources. The traditional text-based materials are commonly used in translation workshops. But in the context of AI, teachers must supplement with more resources that involve the tools and workflows used in the current translation service industry and can collect more teaching materials inspired by AI tools.

3.1.1 Translation Platforms and Tools

A key clement in an AI-powercd translation workshop is the use of AI-driven translation platforms and tools. Several crucial tools and platforms that ought to be incorporated include:

(1) Machine Translation Systems: Students learn the basic knowledge and useful skills of popular NMT systems like Google Translate, DeepL, and Microsoft Translator. These online or offline tools are the typical tools of translation technologies. Teachers and students can practice their post-editing skills after using these NMY systems. Thus the students can summarize the strengths and limitations of machine translation, and the importance of postediting.

(2) Large Language Models: Incorporating LLMs like GPT-4 or other domestic similar models offers students opportunities for the latest advancements in AI technology. These LLMs can be used to generate translations for complex texts, with students assigned the role of assessing the translated works for precision, fluency, and cultural appropriateness.

(3) Computer-Assisted Translation Tools: CAT tools such as SDL Trados, MemoQ, and Dejavu provide students with access to TMs, terminology databases, and real-time collaboration features. They can understand the constraints of human memory and the benefits of machine memory. The use of CAT tools improves students' ability to manage large-scale translation projects while maintaining consistency and efficiency in the team.

3.1.2 Terminology Management Systems

Terminology management is vital for professional translation, guaranteeing consistency throughout translation projects, especially in technical or specialized fields. In the Al-powered workshop, students should learn the theoretical knowledge of terminology management tools like MultiTerm. These tools allow students to create and manage glossaries, ensuring an easy approach to selecting proper terms while maintaining consistency. After guided training, they can understand the importance of maintaining terminological consistency in translation and master the method to realize that.

3.1.3 Corpus Websites and Tools

Corpus websites and tools provide students with access to large databases of parallel texts, allowing them to analyze how particular terms and phrases are used in different contexts. Students can use monolingual corpus like COCA to check the collocation of words and the usage frequency of synonyms. They can still use bilingual corpus tools to extract similar expressions. By learning and practicing these corpus tools, students are aware of the importance of context in translation, and can help them develop a more native understanding of language patterns.

3.1.4 Real-world Translation Projects

To understand how the language service industry uses AI tools in their daily work, the Al-powered translation workshop should simulate the demands of the real translation industry, and students should be given the opportunity to work on actual translation projects. These projects can be sourced from the public, teachers' working experience, or even translation agencies. By translating those projects, students can gain hands-on experience and select the proper combination of AI tools and CAT tools they are learning.

3.2 Teaching Methods and Approaches

Traditional translation teaching methods focus on theoretical knowledge and the practice of language skills or crosscultural awareness. Teachers always forbid their students to use any AI tools in the first phase of translation. Therefore, students lack the chance of post-editing. However, the integration of AI tools into translation workshops brings new translation modes and calls for a shift in teaching methods.

3.2.1 Flipped Classroom

The flipped classroom is exceptionally appropriate for the AI-powcrcd translation workshop. In this approach, students are required to prepare themselves (such as watching online lectures or reading papers on translation technologies) before class, leaving classroom time for more practical and hands-on activities. They come to class to discuss their questions, to present their understandings, and to explain their post-editing ideas, rather than to listen to what their teachers analyze. For example, students may be asked to watch a video on using Trados or DeepL, and then come to class to finish a translation project by these tools. Meanwhile, they can raise any questions aroused in this process. Teachers are more like guides to share solutions. This approach maximizes classroom time for discussion, collaboration, and problem-solving.

3.2.2 Blended Learning

Blended learning is a type of online self-learning and offline face-to-face teaching. It gives students more flexibility in when, where, and how they learn the teaching materials. In an AI-powered translation workshop, blended learning requires students to watch online videos before or after class in accordance with the detailed teaching contents, to do translation exercises on online AI-driven platforms, and to express ideas in online discussions. While offline workshop mainly asks them to carry out translation tasks assigned by teachers and get up-to-date feedback from teachers. This approach allows students to study at their own pace while still being trained by their teachers, and to learn from their peers.

3.2.3 Project-oriented Teaching

Project-oriented teaching is another highly effective way to integrate translation technology into the translation workshop. In this approach, students can be engaged in the overall processes of translation projects, playing different roles like manager, translator, or inspector. These projects can be either individual or collaborative, aiming at simulating real-world translation tasks, including translating a technical manual, localizing a website, or subtitling a video. As required by "clients" or teachers, they have to use the tools and technologies learned in class, including AI tools, MT, CAT, and terminology management systems, to ensure the quality and consistency of their work.

3.3 Practice Frequency

In many traditional translation workshops, due to the limited class time and semester period, most students lack translation practices. In an AI-powered translation workshop, it is crucial to provide students with frequent practice with feedback from peers, teachers, and AI technologies. In addition to traditional assignments, students can select translation practices on the AI platforms designed by the companies and universities, where they arc required to post-edit a text automatically translated by the insert AI systems within a set time. They can choose the level of texts in accordance with their interests and abilities. Teachers just assign the frequency of translation, like twice a week. Such online systems can give scores and analysis in time with the help of AI systems. Thus, teachers can encourage their students to work under pressure and make effective use of the tools at their disposal.

4. Assessment Metric for the AI-powered Translation Workshop

Since more and more students prefer to use AI-powered tools and MT in translation workshops, teachers should think about how student performance can be evaluated by taking AI assistance into consideration. In traditional translation education, teachers evaluate primarily from aspects including linguistic accuracy and cultural awareness. However, in an AI-powered translation workshop, it is also crucial to assess students' degrees in applying translation technologies.

4.1 Comprehensive Assessment System

A comprehensive assessment system can be used by teachers to analyze the translation technology ability students must develop in an AI-powered translation workshop. It should assess linguistic competence, but more importantly, it can evaluate their technical proficiency in different tasks like searching, using CAT software, and so on. Therefore, such assessment system proposed here should have the following components:

(1) Translation Quality: It can compare the machine translation or AI-assistcd translation to the reference translation. And it can detect the degree of AI translation in the works submitted by students.

(2) Technical Skills: It can evaluate the degree of the student's master of using Al-powcrcd translation tools (such as CAT tools, website and computer searching, machine translation, terminology management systems, and corpusbased tools).

(3) Project Management: In addition to the language ability and technology competency, it should also assess the student's ability to manage translation projects, including business negotiation, task and translator allocation, deadline management, advanced management tools or platforms, and collaboration with peers playing different roles like translator, inspector or manager.

(4) Process Evaluation: It can monitor the student's development over a longer time, for example, a semester or two, documenting how they improve their use of tools, their problem-solving strategies, and to what extent their current competencies are.

(5) Self and Peer Assessment: A comprehensive assessment system should encourage students to document their selfreflection on each translation task and peer feedback to get more critical thinking.

This comprehensive approach ensures that students are assessed not only on the final translation works but also on the overall process from both subjective and objective assessment bodies.

4.2 Assessment of Translation Quality

In the traditional assessment of translation quality, teachers primarily focus on text quality. However, in an Al-powcrcd workshop, the criteria for assessing translation quality must be extended from language accuracy to the performance of machine translation or AI-assisted translation. The following aspects should be considered:

(1) Accuracy: The target language text must faithfully convey the meaning of the source language text, including the correct translation of terminology, idiomatic expressions, and cultural or local references.

(2) Fluency: The target language text should read naturally in the target language, in line with the grammatical, syntactical, and stylistic elements of the target language. In addition, logic between the lines must be tested as well.

(3) Cultural Appropriateness: Different countries have their own culture and customs embodied in their languages. Once the cultural elements are considered in the process of translation, it must echo with the target audience and avoid any culture shock or misunderstandings.

4.3 Assessment of Technical Skills

In an AI-powered translation workshop, students must be proficient in using a combination of translation technologies, including MT, CAT tools, terminology management systems, and corpus-based tools.

As machine translation is still indispensable in the language service industry, teachers should assess students' ability to post-edit MT output. Students should have the ability to choose when and how to use machine translation effectively. For example, they should recognize what types of texts can be better translated by MT (e.g., technical manuals) and when human translation is preferable (e.g., literary translation). In addition, students should be able to improve the quality of MT output by detecting typical MT errors and ensuring culturally appropriate translation.

Students' proficiency in using CAT tools is another critical component of the assessment system. They can select one or two CAT tools to create and apply terminology databases to ensure accuracy. They should have the ability to solve problems aroused by CAT tools like error alerts in the process of creating a task.

4.4 Assessment of Project Management Skills

Translation projects in the language service industry often require students to manage multiple tasks simultaneously or to tackle complicated tasks, including using a combination of translation tools, managing project deadlines, and collaborating with team members online and offline. Therefore, students should be assessed on their ability to manage their time effectively by checking their working schedules. They can design the optimal plan of translators and resource allocation, demonstrate the ability to break down translation projects into smaller tasks, and prioritize them appropriately.

In teamwork projects, students must also be assessed on their ability to work effectively with their peers, including communicating effectively with their team members in supervising the progress of project tasks and providing feedback. In addition, students should be able to resolve conflicts that may arise in group projects, such as disagreements over word choices or sentence structures. Such skills should be fully taken into consideration while setting the assessment system.

4.5 Self- and Peer Feedback

A comprehensive evaluation system should include regular feedback on both their translation quality and their use of translation technologies from themselves and their peers, instead of only their teachers.

Teachers should give detailed feedback on students' translation quality, technical skills, and project management abilities. They have to highlight points where students performed well and how to make improvements. For example, if a student struggles with using a particular CAT tool, the teacher should offer suggestions in his feedback.

Peer feedback is important in the assessment system. By reviewing each other's work, students can gain new perspectives while analyzing others' works. Peer feedback can also help students develop critical thinking, as they must detect errors in their peers' translations and provide constructive suggestions for improvement.

Finally, students should be encouraged to reflect on their own performance. This can include reflecting on their use of translation tools, their ability to manage projects, and the quality of their translations.

5. Conclusion

The integration of AI technologies into translation workshops shows a certain degree of advantages, including working efficiency, better translation quality, and a more thorough preparation for the future labor market. However, this change also presents several challenges that must be noticed to guarantee the effective deployment of the AI-powered translation workshop. These challenges include technical difficulties and the imbalance among students, teachers, and AI tools.

The AI-powered translation workshop requires access to many translation tools and platforms, many of which are expensive or require a specialized classroom environment. Besides, students and teachers may encounter technical barriers as technology keeps updating, for example, software compatibility issues, system update requirements, or limited access to better tools due to school budget constraints.

In addition, teachers should balance the teaching of traditional translation skills with the use of AI tools, emphasizing the importance of critical post-editing. Teachers can train their students to remain actively engaged in the translation process rather than passively accepting machine- or AI-generated target language text. Then, their students can properly view AI tools or other translation technologies as personal assistants rather than replacements.

There still are concerns about data privacy and security, particularly when translating sensitive or confidential documents. Teachers and students should understand the importance of data security and the ethical issues.

By providing open access to translation technologies and offering certain guidance in and after class, teachers can create a responsible and sustainable approach to integrating AI tools into translation education, so as to improve the training of professional translators for the future language service industry.

Funding

2023 Educational and Teaching Reform Research Project of Xijing University: Exploration and Practice of a "Digital Intelligence" Powered Translation Workshop Teaching Model Driven by the "Translation + Technology" Factors (Project No. JGYB2352).

Sidebar

How to cite this paper: Jing Ning, Haidong Ban. (2024). Application of Translation Technology in AI-powered Translation Workshop. The Educational Review, USA, 8(10), 1242-1249. DOI: 10.26855/er.2024.10.008

Received: September 24, 2024

Accepted: October 19, 2024

Published: November 18, 2024

Corresponding author: Jing Ning, Xijing University, Xi'an 710123, Shaanxi, China.

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