Academic literature on the topic 'Google Translation API'

In the last decade, the plagiarism cases were increased and become a topical problem in the modern scientific world, caused by the quantity of textual information available online/offline. The authors' work deals on the development of a new plagiarism detector system called BHA2 which has as input the suspicious text (to be analysed) and the original texts (learning basis). It can detect the different forms of plagiarism based on: Google API to detect the cases of plagiarism with translation; text summarization to detect the plagiarism of idea; conceptual transformation to detect the plagiarism with synonymy; bag of phrases to detect the paraphraser plagiarism; the social worker bees algorithm that was inspired from the lifestyle of social worker bees (forager, guardian, and cleaner) to select the documents source of plagiarism; the output of the authors' system are the plagiarised passages (the copied parts from the original texts) and the plagiarism percentage for each suspicious text. Their experiments were performed on the Pan 09 dataset and using the validation measures (recall, precision, accuracy, error, f-measure, and entropy, FPR, FNR, W-accuracy, ROC and TCR) in order to show the benefit derived from using such idea compared to the result of classical systems existed in literature. A comparative study in term of services was realised between their system and others commercial systems such as (check, Turnitin, and machine learning system) with their system. Finally, a visualization step was achieved for the purpose to see the outcome in graphical form (3d cub and cobweb) with more realism using the functionalities of zooming and rotation.

The past decades have witnessed remarkable progress in machine translation (MT) quality, sparking a heated debate within and beyond academia about whether human translators will be replaced by MT systems. This case study conducts a genre-based manual quality evaluation of Chinese-to-English translations produced by Google Translate, Baidu Translate, Sogou Translate, Youdao Translate, and human translators. The findings reveal that the quality of online machine translation output remains incomparable to that of human translation across expressive, informative, and vocative genres. Sogou Translate excels in expressive and informative genres but requires significant improvements in fidelity and comprehensibility for expressive genres, as well as in fidelity and genre function reproducibility for informative genres. For vocative genres, the quality of online machine translation varies across the four evaluation criteria and necessitates targeted enhancements. Notably, Google Translate, despite its reputation as a leading machine translation system, lags behind others in reproducing genre-specific functions and ensuring comprehensibility. The study suggests that machine translation developers should leverage advanced AI language technology to develop an AI-assisted MT training system capable of interpreting nuances for more sophisticated translations.

We have entered into the era of artificial intelligence, neural machine translation, and especially large language models which have dramatically changed the landscape of human translation practices. On the one hand, translation technology has remarkably improved translation efficiency and reduced the cost of translation; on the other, it has brought about some issues related to translation quality, translator competence, translation ethics, and so on. This offers fertile ground for research on translation technology used in different scenarios. The eight chapters in this volume can be divided into two groups: the contributions by Chinese authors and those by authors from other countries. Among the Chinese contributors, Wang Fang makes a comparative study of the Chinese-English translations by ChatGPT Translate and Baidu Translate, concluding that ChatGPT’s human-computer interaction-based translation will bring new insights from the AI era to translation practitioners, translation learners, and technology developers. Yang Qiong makes a case study of the genre-based manual quality evaluation of Chinese-English translations produced by Google Translate, Baidu Translate, Sogou Translate, Youdao Translate, and human translators, suggesting that machine translation developers should leverage advanced AI language technology to develop an AI-assisted MT training system capable of interpreting nuances for more sophisticated translations. Zhang Junchi explores the use of ChatGPT in translation teaching, suggesting that neither teachers nor students should rely on ChatGPT too much, they have to think independently and judge whether the information is desirable, and a correct understanding of the interaction between translation teaching and technology should be fostered. Wang Juan makes a CiteSpace-based bibliometric analysis of the Web of Science (WoS) core collection publication (2000-2024) on translation technology, highlighting prominent scholars and institutions, foundational literature, thematic areas, developmental trajectories, and prospective directions in the realm of translation technology. Cui Cui investigates the Chinese student interpreters’ usage intention of ChatGPT-assisted translation with Q methodology, providing some pedagogical implications of learning interpreting for university students. As far as the contributors from other countries are concerned, Adelinde H. Meyer explores AI-powered quality assurance (QA) tools and techniques, reviewing how AI and ML refine translation quality management, examining the implications of AI in translation for stakeholders like translators, clients, and academics, and focusing on the challenges and prospects AI brings to the translation industry. Prateek Kumar reviews the main types of MT that have been proposed and developed over time: rule-based, statistical, hybrid, and neural MT, aiming to present an overview of the state-of-the-art in MT, identify key challenges and issues that MT faces today and tomorrow, and outline some possible ways out of them. Ecrin Yilmaz discusses how AI technologies, especially machine translation and natural language processing, have transformed the landscape of translation quality by making available automated support tools to translators, explaining the evolving roles of translators and clients, as well as AI acting as a mediator, pointing out some challenges, such as ethics considerations, data privacy, and biases, as well as some opportunities such as multilingual content creation and cross-cultural communications.

The public availability of ChatGPT and other AI tools is having a transformative effect on our lives in a variety of domains including education. Many language learners have for some time been avid users of AI-enabled tools such as Google Translate, text editors like Grammarly, or voice assistants. While some uses of AI products in L2 teaching and learning have generally been seen positively, the use of others has been controversial. This chapter addresses issues surrounding the integration of AI tools into instructed SLA (second language acquisition), focusing on machine translation, chatbots, and AI-based tools for written corrective feedback. It suggests that, based on existing research studies and reports on teaching practices using AI tools, L2 instructors should adopt a critical, balanced approach to AI integration in L2 instruction that leverages generative AI’s strengths while taking into account its downsides.

People around the world have come to rely on digital maps–accessible with the tap of a smartphone screen–to guide them places, to locate areas with which they are unfamiliar, and to get directions to desired destinations. In a world that is overflowing with technological advancements, we expect to be shown, in real time, how to get from point A to point B. However, navigating indoors using assistive technology can prove more challenging than navigating outdoors, as indoor spaces have not traditionally been mapped the way outdoor spaces have by companies like Google and Apple. Additionally, mos