Human Translation Research Articles

Artificial Intelligence Impact on Human Translation: Legal Texts as a Case Study

The recent paper highlights the impact of artificial intelligence on Machine Translation without the interaction of Humans. The use of Google Translator, Bing, Microsoft Translator, Systran Translate and Amazon Translate has become widely spread (CAT Tools). This study aims to reveal the contrast between Artificial Intelligence and Human Translation in the legal field. A hypothesis of the difference between Artificial Translation and Human Translation was raised. The concerns about the lack of a translator increased, and machine translation was selected as the most selected option. Local and foreign contracts were selected and subjected to Human and Machine translation. Strengths and weaknesses points were selected and analyzed. The previous studies in the legal translation field were considered. The results revealed the gap between human translation and machine translation, and human translation is dominant in the light of accuracy and the existence of legal language. The findings also focused on the Translators' experience and knowledge in the translation field.

Preservation of sentiment in machine translation of low-resource languages: a case study on Slovak movie subtitles

AbstractThis research explores the effectiveness of machine translation from Slovak to English for sentiment analysis, specifically focusing on the translation of movie subtitles. The study employs a parallel corpus of segmented movie subtitles in both languages and utilizes IBM Watson™ Natural Language Understanding service and Google Translate. The research aims to assess the correlation between human-generated text and machine-translated text in sentiment analysis. A comparative analysis was also conducted using OpenAI to evaluate the sentiment of the Slovak text directly, without translation into English. The findings reveal a strong correlation between human text and machine translation, with a Pearson correlation coefficient of 0.86, and a correlation with OpenAI’s GPT model evaluation at 0.72. Despite the relatively high accuracy of the end-to-end solution using OpenAI, the methodology comprising machine translation followed by sentiment analysis in English was found to be significantly more precise. The research further investigates the challenges in translating specific language nuances, such as humor and vulgarism, and their impact on sentiment analysis. The study concludes that machine translation can be effectively used for sentiment analysis in Slovak, a flective language, and highlights the potential of advanced language models in low-resource languages. Future research directions include expanding the study to other text types and comparable languages beyond Slovak.

Found in translation, lost in education: artificial intelligence’s impacts on translation tertiary education in Macao

PurposeTo explore the interplay between human translators and AI tools, focusing on tertiary students' perceptions in the context of Portuguese-Chinese translations in Macao.Design/methodology/approachThis research employed a mixed-methods approach. Quantitative surveys were complemented by qualitative responses. Qualitative class observations (participant and non-participant) and autoethnography further enriched the insights. Participants included undergraduate and postgraduate students in translation studies from the Macao Polytechnic University.FindingsThe data revealed a dual perspective: appreciation for AI’s efficiency contrasted with concerns about its potential to overshadow human touch in translations, especially in cultural nuances. Views on integrating AI into curricula were diverse, but a balanced, synergistic approach between human expertise and AI efficiency emerged as a common theme.Originality/valueThis study offers a fresh perspective by integrating various methodologies, capturing both statistical and experiential insights on the evolving relationship between AI and human translation efforts in academia.

Bilingual Translation in Spiderman the Movie Storybook

The language transfer can be seen in the lexical choices in a translation. A translator may have different considerations when applying its use in a text since adaptation of context is an essential part to be employed through the translation. Problems with transferring the source language into the target language certainly have an effect on delivering messages within a text. This study aims to analyze the lexical usage in the bilingual translation of the movie storybook Spiderman and the strategies in the translation, including their meaning. The qualitative method used DeepL to assist in the analysis of differences that might come into the translation. In some cases, words translated from English into Indonesian were found non-equivalent, however, the rest of them were understandable. Translation by a more general word strategies were also used to be placed in the story rather than using the specific words and vice versa, paraphrasing using a related word, omission, and neutral words or less expressive words found to correspond the meaning. The equivalence of transferring the source language (SL) into the target language (TL) can be achieved when optional words are found in the translation in which meaning is also taken for consideration within the human translation. The product of translation was presented bilingually to analyze the differences in meaning between the two languages’ context of use. These showed DeepL as one of the machine translations with translation memories using Artificial Intelligence technology which compares the differences and similarities of sense in translation study. They can work together for the analysis to understand the use and results. Keywords: equivalence, non-equivalence, lexical, strategy

Examining the Cultural Connotations in Human and Machine Translations: A Corpus Study of Naguib Mahfouz's Zuqāq al-Midaqq

The translation of culture-specific terms constitutes a major challenge for professional translators as it necessitates a thorough understanding of both the linguistic and cultural elements. With rapid technological advancement over the past few years, machine translation has enhanced translation quality. This study investigates the transability of cultural connotations in the Arabic-English translation of Naguib Mahfouz’s Zuqāq al-Midaqq. A descriptive qualitative research design was adopted to achieve the intended goals of the study. The data comprised human and machine translations from Google Translate and ChatGPT. Through qualitative content analysis, the translations were compared for accuracy in transferring the cultural connotations prevalent in the Arabic source text. The findings revealed that the human translation showed greater naturalness and accuracy in rendering cultural connotations. Machine translation has struggled with rhetorical devices, idioms, and cultural nuances. The results also indicated that the AI-enhanced machine represented by ChatGPT captured the cultural elements more effectively than Google Translate. The study concluded that human expertise remains essential for the high-quality translation of literary works to maintain cultural significance. The findings can inform translator training and guide improvements to AI-enhanced translation for literary texts.

Evaluating optimal reference translations

Abstract The overall translation quality reached by current machine translation (MT) systems for high-resourced language pairs is remarkably good. Standard methods of evaluation are not suitable nor intended to uncover the many translation errors and quality deficiencies that still persist. Furthermore, the quality of standard reference translations is commonly questioned and comparable quality levels have been reached by MT alone in several language pairs. Navigating further research in these high-resource settings is thus difficult. In this paper, we propose a methodology for creating more reliable document-level human reference translations, called “optimal reference translations,” with the simple aim to raise the bar of what should be deemed “human translation quality.” We evaluate the obtained document-level optimal reference translations in comparison with “standard” ones, confirming a significant quality increase and also documenting the relationship between evaluation and translation editing.

Application of ELAN in Human-Machine Mutual Translation of English for the News

The translation of English news means the process of converting news articles published in English into another language. These articles may also be referred to as English news. This process comprises understanding the substance of the source article and accurately translating its tone and intent into the target language. A human translator or machine translation software can be used to perform the translation manually. In the disciplines of software globalization and technological translation, the Human-Machine Mutual Translation technique has grown significantly with the advancement of civilization and the quickening growth of research and innovation. Because of this practical need, translators have begun to focus more on machine translation and have conducted some productive research in this area. As a result, we suggested the EUDICO Linguistic Annotator (ELAN) in Human-Machine mutual translation of English for the news. EUDICO stands for European Distributed Corpora Project. The Max Planck Institute for Psycholinguistics created the EUDICO Linguistic Annotator as a software program for the linguistic annotation of language data. For this study, the English news dataset was used. The data were pre-processed by using normalization. The results were evaluated using performance metrics such as translation quality, precision, BLEU, recall, and computation time. The findings show that the recommended procedure provides an accurate translation of English news when compared to previously used methods. The results demonstrated that translation quality and computation time of proposed model are 95% and 60s respectively.

Analysis of Address Terms Translation Strategies: The Norms in the Translation of Address Terms in Moment in Peking

This paper, framed within Chestermans translation ethics, focuses on the translation of address terms in the first volume of Moment in Peking. It analyzes the practical application of professional norms and expected norms in the translation of novels, exploring the strategies of address terms translation. Through comparative analysis of different Chinese translations of address terms in the first volume of Moment in Peking (translated by Zhang Zhenyu, Yu Fei, and machine), this paper aims to reveal the ethical and professional norms that translators should consider in the selection of address terms and explore the performance of machine translation in terms of norms. The study finds that the ethical principles of human translators result in higher quality and reliability of translations, while machine translation, although formally faithful, still has deficiencies in context perception, emotional discernment, and target reader cognition.

An Evaluation of ChatGPT's Translation Accuracy Using BLEU Score

Traditional views have long held that machine translation cannot achieve the quality and accuracy of human translators, especially in complex language pairs like Persian and English. This study challenges this perspective by demonstrating that ChatGPT-4, with access to vast amounts of multilingual data and leveraging advanced large language model algorithms, significantly outperforms widely utilized open-source machine translation tools and approaches the realm of human translation quality. This research aims to critically assess the translation accuracy of ChatGPT-4 against a traditional open-source machine translation tool from Persian to English, highlighting the advancements in artificial intelligence-driven translation technologies. Using Bilingual Evaluation Understudy scores for a comprehensive evaluation, this study compares the translation outputs from ChatGPT-4 with MateCat, providing a quantitative basis for comparing their accuracy and quality. ChatGPT-4 achieves a BLUE score of 0.88 and an accuracy of 0.68, demonstrating superior performance compared to MateCat, with a 0.82 BLUE score and 0.49 accuracy. The results indicate that the translations generated by ChatGPT-4 surpass those produced by MateCat and nearly mirror the quality of human translations. The evaluation demonstrates the effectiveness of OpenAI's large language model algorithms in improving translation accuracy.

Comparison Between Human Translation and Machine Translation Take Lolita as an Example

Taking the excerpt of Lolita as the research corpus, this paper compares the differences between the manual translation and the machine translation, and finds that the machine translation system has some problems such as inaccuracy in semantic understanding and grammatical errors. In order to improve the quality of machine translation and promote cross-cultural communication and information flow,a classification system of translation errors is established and the errors in machine translation are summarized in detail. The proposed classification system will facilitate a comprehensive understanding of the limitations inherent in machine translation, enable an in-depth analysis of existing challenges, and serve as a valuable reference for enhancing the performance of machine translation systems, thereby elevating the quality and precision of automated translations.

Future Translators’ Linguistic and Non-linguistic Competencies and Skills in The Age of Neural Machine Translation and Artificial Intelligence: A Content Analysis

Artificial Intelligence (AI) and machine learning (ML) are disrupting the way millions of people work. According to the Future of Jobs Report published by the World Economic Forum (WEF 2022), the Fourth Industrial Revolution will result in the disappearance of more than 75 million jobs by 2025. While this is considered bad news to many, the report also predicts the creation of 133 million jobs for the same reason. Among the at-risk jobs are translation and language services jobs which have witnessed a sharp drop in demand due to the adoption of AI and ML technologies in machine translation systems making them capable of producing translations that match the translations produced by expert human translators. Using content analysis method of online translation job advertisements in Saudi Arabia, this study sheds light on the current in-demand translator linguistic and non-linguistic competences and skills from the perspective of employers and matches those with the well-established translator competence models in literature. Through a content analysis of 213 online job advertisements, the study identifies five key competency domains: Language and Culture, Personal and Interpersonal, Service Provision, Technology, and Translation Knowledge. While language proficiency remains crucial, there is a growing emphasis on "soft skills," project management, technological adaptability, and ethical practice. The findings reveal a partial alignment with existing competency models but also highlight potential gaps, particularly regarding strategic planning and document analysis skills. The study underscores the need for translator education to adapt to the evolving demands of the industry, integrating traditional skills with emerging technologies and fostering a culture of lifelong learning. By bridging the gap between academic training and industry expectations, we can better equip future translators to navigate the complexities of the profession and thrive in the age of Neural Machine Translation and AI.

Exploring user perspectives

Abstract Since the advent of Google NMT in 2016, human translators have been overwhelmed by the concern about being replaced by machine translation. Although professional translators argue that the machine translation output is not refined enough to surpass human translators, their claims are sometimes emotional and based on incorrect perceptions, without verification and substantiation from user evaluation and specific quality evaluation data. Therefore, this study examines the evolution of NMT output from English to Korean diachronically and provides specific user evaluation data that can verify and substantiate the claims. Despite the steady improvement in NMT performance observed in recent years, it has been recognized that there is still a significant gap that must be bridged for NMT to achieve parity with human translation. Nevertheless, as the collaboration between NMT and human translators is expected to increase, it is essential to reinforce relevant systems, such as technological and legal support systems.

Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms

This study examines the advancements in AI-driven machine translation, specifically focusing on the accurate translation of Arabic colloquial expressions. It aims to assess the progress made by Large Language Models, such as Bing AI Chat, compared to traditional machine translation systems. By focusing on colloquial expressions, this research aims to shed light on the challenges and opportunities for improvement in machine translation systems, particularly when dealing with the complexities of translating informal Arabic utterances. Building upon At-tall’s 2019 thesis, which compared Google Translate and human translators, the study employs the same Arabic sentences as a test dataset, allowing for a direct comparison between 2019 translations and those produced by current machine translation tools. The findings indicate limited improvement in Google Translate since 2019, with Bing Translator exhibiting a similar level of translation accuracy. In contrast, Bing AI Chat consistently outperformed the other systems, showcasing the potential of Large Language Model machine translation. Notably, Bing AI Chat provided interpretations and valuable comments on the tested Arabic phrases, demonstrating a deeper understanding of the intended meaning. This study contributes significantly to the field of machine translation by providing evidence of the potential of Large Language Model systems in producing more accurate Arabic-English translations. It emphasizes the advantage of Large Language Models in dealing with non-standard Arabic expressions, encouraging further exploration of Large Language Model-powered approaches in machine translation. The findings offer a promising pathway towards achieving more accurate and expressive translations across diverse languages and cultures.

Comparative assessment of Bing Translator and Youdao Machine Translation Systems in English-to-Chinese literary text translation

This study explores the performance of machine translation of literary texts from English to Chinese. The study compares two machine translation systems, Bing Translator and Youdao Machine Translation, using selected texts from the novel “Nineteen eighty-four” by George Orwell. The data collection includes the original source texts, their machine-generated translations by Bing Translator and Youdao Machine Translation, and comparisons with human reference translations to assess the performance of these systems. The research’s focal point is to evaluate the accuracy, fluency, and appropriateness of translations generated by these two machine translation systems, while also analyzing the post-editing effort required to enhance the quality of the final machine-translated product. The study revealed that despite the presence of flaws in both machine translation systems, Youdao Machine Translation demonstrated superior performance, especially in accurately translating technical terms and idiomatic expressions, making it the more effective option overall. Nevertheless, the translations from Youdao Machine Translation required more substantial post-editing efforts to improve fluency and readability. Conversely, Bing Translator yielded more fluent and natural-sounding translations, albeit with a need for improved accuracy in translating technical terms and idiomatic expressions. The study concludes that while machine translation systems are capable of generating reasonable translations for literary texts, human post-editing remains essential to ensure the final output’s accuracy, fluency, and appropriateness. The study underscores the importance of selecting the appropriate machine translation system based on the nature of the text being translated. It also highlights the critical role of post-editing in refining the quality of machine-translated outputs, suggesting that while machine translation can provide a solid foundation, human intervention is indispensable for achieving optimal accuracy, fluency, and overall readability in literary translations.

Comparing translation accuracy in Belt and Road Malaysia children's literature: Malay and Chinese native speakers vs ChatGPT

The study investigates the translation processes of human and artificial intelligence translators in comparison. Human translators consist of a Chinese native speaker and belt and road translators. Different versions of artificial intelligence translators comprise ChatGPT 3.5 and ChatGPT 4.0. The research methodology employed is a keyword detection technique. One human translator and one translator powered by artificial intelligence achieved the highest scores in keyword detection, according to the results. Human translators continue to be indispensable in the field of translation, particularly in the translation of literary works. However, the research group is optimistic that artificial intelligence will soon be able to resolve this issue.

The Effectiveness of ChatGPT in Translating Chunky Construction Texts in Chinese Political Discourse

From the theoretical perspective of Chinese-English temporal-spatial cognitive differences, this study employs a self-constructed bilingual parallel corpus of political discourse totaling one million words. It extracts chunky construction texts with latent agents in the source language (Chinese) and corresponding texts of target language translations (English) for a comparative experiment between ChatGPT translations and those of human translators. Driven by the “Human-AI Interaction Model”, three different prompts were set up to conduct three rounds of translation testing with ChatGPT. The study discovers: 1) ChatGPT has limitations in actively understanding the implicit elements of the source Chinese texts that are driven by a strong spatial preference, which are also reflected in the target language English translations; 2) The “Human-AI Interaction Model” can guide and train ChatGPT effectively through continuous optimization of prompts, enhancing AI abilities in understanding the source language and achieving effective target language translation; 3) Currently, ChatGPT cannot replace human translators in translating chunky discourses of Chinese, but the collaborative interaction between human translators and AI is highly effective. By optimizing the design of prompt instructions, this research offers cognitive reference and operational paradigms to improve ChatGPT’s recognition and cognitive reconstruction capabilities of Chinese-English temporal-spatial differences, providing insights for future research, translation practice, and teaching in related areas.

Evolution and Development of Artificial Intelligence Interpretation Technology in the Age of Large-scale Language Models

With the continuous development of artificial intelligence technology, large-scale language models have become an important breakthrough in the field of interpreting intelligence technology, and the interpreting profession is facing unprecedented technological changes; Through reviewing the relevant research background, it was found that the rise of large models has brought new opportunities and challenges to the development of artificial intelligence interpreting technology; Firstly, the language generation ability of the large model is powerful, which can achieve more accurate and natural interpretation and translation results. Secondly, large models have strong learning abilities and can continuously optimize the performance of interpretation systems through large-scale data training. In addition, the large model also has the characteristic of fast response, which can provide efficient interpretation services in real-time scenarios; The age of large models provides new opportunities and prospects for the transformation and development of artificial intelligence interpretation technology. In the future, with the continuous evolution of large model technology and the expansion of application scenarios, artificial intelligence interpretation technology is expected to play a more important role in cross language communication, international cooperation, and other aspects. However, attention should also be paid to the application limitations of large models and issues such as collaboration with human translators.

An improved method for generating human spinal cord neural stem cells

Neural stem cells have exhibited efficacy in pre-clinical models of spinal cord injury (SCI) and are on a translational path to human testing. We recently reported that neural stem cells must be driven to a spinal cord fate to optimize host axonal regeneration into sites of implantation in the injured spinal cord, where they subsequently form neural relays across the lesion that support significant functional improvement. We also reported methods of deriving and culturing human spinal cord neural stem cells derived from embryonic stem cells that can be sustained over serial high passage numbers in vitro, providing a potentially optimized cell source for human clinical trials. We now report further optimization of methods for deriving and sustaining cultures of human spinal cord neural stem cell lines that result in improved karyotypic stability while retaining anatomical efficacy in vivo. This development improves prospects for safe human translation.

Reference in English-Chinese Legal Translation: Human Translators Versus ChatGPT

Reference is a device of grammatical cohesion; it refers to another element with the same semantic meaning; it can be categorized into personal reference, demonstrative reference, and comparative reference (Halliday & Hasan, 1976). This paper aims to delve into the grammatical cohesion in legal translation, focusing particularly on the translatability of reference in English-Chinese translation through a detailed case study. To this end, the International Code for the Protection of Tourists is chosen as the source text and is compared with two target Chinese translations: one by human translators and the other by ChatGPT (the fourth version). The data related to differences of reference items between English and Chinese are identified and marked. Based on Toury’s (2012) framework of descriptive translation studies, the study is qualitative and is conducted on the English-Chinese translation of reference in the legal text, the human translation, and the ChatGPT’s translation. The research reveals that three translation methods (literal translation, omission, and amplification) are used in translating reference items. It indicates that there are more advantages of human translators than ChatGPT in legal translation.

Translating Ulyssei@s: Work in progress between general and specialised translation in light of NMT

It is common knowledge that the publication of translation projects carried out by students is a very motivating factor for training. Since the second semester of 2015-2016, undergraduate translation students of the Faculty of Arts and Humanities of the University of Porto have participated in the development of the French and English versions of the digital encyclopaedia Ulyssei@s, created by the Institute for Comparative Literature Margarida Losa (ILCML) in the early 2010s. Besides motivating students, this translation project published on the Internet allows us to develop translation skills, foster teamwork, consolidate the three-phase methodology proposed by Gouadec (2002, 2007), and experiment with several essential tasks in language services: collaborative glossary-making, translation, revision of human translation and post-editing neural machine translation (NMT). Some of the challenges posed by the translation of Ulyssei@s are common to all types of translation, while others are specific to the translation of entries of this sui generis encyclopaedia, such as a variety of textual genres (biography, literary criticism, prose, poetry, theatre, essay) in hybrid texts and the need to find the original or the official translation of the works cited. This paper focuses on the data collected in the spring semester of 2022 and is based on the results of a survey submitted to students at the end of the project. It aims to share the experience of developing an inevitably unfinished project and adapting training and learning to the latest advances in Human Language Technology, since NMT has become a valuable tool for human translators, and Ulyssei@s is a digital encyclopaedia regularly enriched by ILCML collaborators with new entries.