Google Translate Research Articles

An analysis of Google Translate and DeepL translation of source text typographical errors in the economic and legal fields

Training neural machine translation systems with noisy data has been shown to improve robustness (Heigold et al., 2018). The objective of the present study is to test Google Translate and DeepL performance in the detection and correction of typographical errors, by introducing 1,820 source text typos found in a previous work on specialised Spanish-English dictionaries (Rodríguez-Rubio & Fernández-Quesada, 2020a, 2020b; Rodríguez-Rubio Mediavilla, 2021). Typos were introduced in isolation and also in co-text. Results showed that Google Translate clearly outperformed DeepL. Moreover, the repetition of the source typo was found to be the most frequent phenomenon in the machine translation output of both systems. By shedding light on the capacity of systems to deal with source text typographical errors, our study aims to provide a starting point for their refinement.

Performance of ChatGPT and Google Translate for Pediatric Discharge Instruction Translation

BACKGROUND AND OBJECTIVES Patients who speak languages other than English face barriers to equitable healthcare delivery. Machine translation systems, including emerging large language models, have the potential to expand access to translation services, but their merits and limitations in clinical practice remain poorly defined. We aimed to assess the performance of Google Translate and ChatGPT for multilingual translation of pediatric discharge instructions. METHODS Twenty standardized discharge instructions for pediatric conditions were translated into Spanish, Brazilian Portuguese, and Haitian Creole by professional translation services, Google Translate and ChatGPT-4.0, and evaluated for adequacy (preserved information), fluency (grammatical correctness), meaning (preserved connotation), and severity (clinical harm), along with assessment of overall preference. Domain-level ratings and preferred translation source were summarized with descriptive statistics and compared with professional translations. RESULTS Google Translate and ChatGPT demonstrated similar domain-level ratings to professional translations for Spanish and Portuguese. For Haitian Creole, compared with both Google Translate and ChatGPT, professional translations demonstrated significantly greater adequacy, fluency meaning, and severity scores. ChatGPT (33.3%, P < .001) and Google Translate (23.3%, P = .024) contained more potentially clinically significant errors (severity score ≤3) for Haitian Creole than professional translations (8.3%). Professional Haitian Creole (48.3%) and Portuguese (43.3%), but not Spanish (15%), translations were most frequently preferred among translation sources. CONCLUSIONS Machine translation platforms have comparable performance to professional translations for Spanish and Portuguese but shortcomings in quality, accuracy, and preference persist for Haitian Creole. Diverse multilingual training data are needed, along with regulations ensuring safe and equitable applications of machine translation in clinical practice.

Machine Translation Testing via Syntactic Tree Pruning

Machine translation systems have been widely adopted in our daily life, making life easier and more convenient. Unfortunately, erroneous translations may result in severe consequences, such as financial losses. This requires to improve the accuracy and the reliability of machine translation systems. However, it is challenging to test machine translation systems because of the complexity and intractability of the underlying neural models. To tackle these challenges, we propose a novel metamorphic testing approach by syntactic tree pruning (STP) to validate machine translation systems. Our key insight is that a pruned sentence should have similar crucial semantics compared with the original sentence. Specifically, STP (1) proposes a core semantics-preserving pruning strategy by basic sentence structures and dependency relations on the level of syntactic tree representation, (2) generates source sentence pairs based on the metamorphic relation, and (3) reports suspicious issues whose translations break the consistency property by a bag-of-words model. We further evaluate STP on two state-of-the-art machine translation systems (i.e., Google Translate and Bing Microsoft Translator) with 1,200 source sentences as inputs. The results show that STP accurately finds 5,073 unique erroneous translations in Google Translate and 5,100 unique erroneous translations in Bing Microsoft Translator (400% more than state-of-the-art techniques), with 64.5% and 65.4% precision, respectively. The reported erroneous translations vary in types and more than 90% of them are not found by state-of-the-art techniques. There are 9,393 erroneous translations unique to STP, which is 711.9% more than state-of-the-art techniques. Moreover, STP is quite effective in detecting translation errors for the original sentences with a recall reaching 74.0%, improving state-of-the-art techniques by 55.1% on average.

Design and Implementation of English to Yorùbá Verb Phrase Machine Translation System

The Yorùbá ethnic group has diverse language speakers across the world, and translating the language to other widely spoken languages must be emphasized. This study aims to develop an English to Yorùbá machine translation system which can translate the English verb phrase text to its Yorùbá equivalent. Words from both languages (Source Language and Target Language) were collected for the verb phrase group in the home domain. Lexical translation was done by assigning values of the matching word in the dictionary. The syntax of the two languages was realized using Context Free Grammar, the rewrite rules were validated with finite state automata. Human evaluation method was used and expert opinion scored. The evaluation shows the system performed better than that of sampled Google translation with over 70% of the responses matching that of the system’s output.

A Comparative assessment of ChatGPT vs. Google Translate for the translation of patient instructions

A Comparative assessment of ChatGPT vs. Google Translate for the translation of patient instructions

Large language models "ad referendum": How good are they at machine translation in the legal domain?

This study evaluates the machine translation (MT) quality of two state-of-the-art large language models (LLMs) against a traditional neural machine translation (NMT) system across four language pairs in the legal domain. It combines automatic evaluation metrics (AEMs) and human evaluation (HE) by professional translators to assess translation ranking, fluency and adequacy. The results indicate that while Google Translate generally outperforms LLMs in AEMs, human evaluators rate LLMs, especially GPT-4, comparably or slightly better in terms of producing contextually adequate and fluent translations. This discrepancy suggests LLMs' potential in handling specialized legal terminology and context, highlighting the importance of human evaluation methods in assessing MT quality. The study underscores the evolving capabilities of LLMs in specialized domains and calls for reevaluation of traditional AEMs to better capture the nuances of LLM-generated translations.

Google and Legal Translation: The Case Study of Contracts

In recent years, the need for Machine Translation (MT) has grown, especially for translating legal contracts between languages like Arabic and English. This study primarily investigates whether Google Translator can adequately replace human translation for legal documents. Utilizing a widely popular free web-based tool, Google Translate, the research method involved translating six segments from various legal contracts into Arabic and assessing the translations for lexical and syntactic accuracy. The findings show that although Google Translate can quickly produce English-Arabic translations, it falls short compared to professional translators, especially with complex legal terms and syntax. Errors can be categorized into: polysemy, homonymy, legal doublets, and adverbs at the linguistic level, and morphological parsing, concord, and modality at the syntactic level. The study concludes with recommendations for enhancing machine translation systems and suggests caution in using Google Translate for legal purposes, advocating for continued reliance on human expertise in legal settings.

Analysis of Ambiguity, Vagueness, Fuzziness, Uncertainty, Possibility and Probability in the Natural Language Semantics with Fuzzy Logic

Understanding the esotericism of human instinct in their daily life conversation is not enough then a mystery now. This is a bundle of ambiguity, vagueness, fuzziness, uncertainty, possibility and probability as a wrap that humans have built around themselves. With the advancement in artificial Intelligence, natural language processing is more capable now to work with real world and performing intelligent analysises. The real world has interactions between natural and artificial intelligent systems. Despite all it, humans retained their superiority over artificial intelligent systems. The fuzzy Logic can play an important computational role in understanding this intelligence gap in clear dimensions. Logical Semantics, Distributional Semantics and Probabilistic Logic are focused on their intention for better natural language semantic representations. But no single semantic representation fulfills all requirements needed for a satisfactory representation. The objective of the present work has two folds. The first one focused on the understanding of fuzzy logic in two dimensions as an intelligence computational technique and another as mathematical modeling of natural language semantics. The second fold illustrates this intelligence gap with real world examples of natural language processing applications such as Google and Microsoft Translator.

Enhancing Post-Editing Machine Translation Skills Among Iraqi Undergraduate Students

Post-editing machine translation often results in discrepancies between industry expectations and the actual quality of the final product. Despite the usually, low-quality outcomes associated with machine translation, it remains underutilized, even when there are opportunities for improvement through post-editing jobs. This study focuses on the impact of technology-assisted training on English proficiency among students. It investigates a standard error observed among Iraqi students during the post-editing of machine translations using Google Translate deciding whether to edit the machine translation output or to translate from scratch and understand the source text. This research involved 20 undergraduates from Al-Mustansiriyah University and employed a mixed methods approach, collecting data through an online questionnaire completed by students in the undergraduate translation program. The findings indicate a significant improvement in the frequency of post-editing skills among Iraqi students, serving as a distinguishing factor between the two groups. It also highlighted differences in translation abilities among students in Iraqi higher education, with notable improvements observed in the experimental group. In summary, this study supports previous research from the last five years, indicating a more favourable attitude toward machine translation and suggesting that post-editing is considered an essential sub-competency in translation education.

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.

Students Perception of Using Google Translate in Learning English

The purpose of this study is to determine students' perceptions of the use of Google Translate in English learning, in terms of absorption, understanding, and evaluation. This study used quantitative methods with descriptive design analysis. The average score is used to answer the research question. The instrument used in this study was a questionnaire. Responded to this study were grade 7 students in one of the junior high schools in Airmadidi North Minahasa totaling 173 students. The results of the analysis showed that students agreed that absorption factors influenced students' perceptions of the use of Google Translate in learning English. Students agree that comprehension factors influence students' perceptions of using Google Translate in English language learning. Students strongly agree that evaluation factors influence students' perceptions of the use of Google Translate in English language learning. So, it can be concluded that students' perception of the use of Google Translate is positive judging from the factors that affect the use of Google Translate. However, students should use Google Translate as a useful additional tool to understand English Lessons better

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.

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.

The Gaza-Israel War Terminology: Implications for Translation Pedagogy

Student translators at my college take a Media Translation course in which they translate the latest news stories. This study proposes a model for integrating Gaza-Israeli war terminology and texts in translation instruction to familiarize the students with such terminology such as (names of weapons (grenades, mortar, drones, missiles, Merkava, Cornet anti-armor, mortar shells), toponyms (Khan Younis, Maghazi, Sderot, Ashkelon), crossings (Rafah, Erez), Jihadist groups and brigades (Islamic Jihad, Golani), military actions (incursion, bombing, shelling, genocide, displacement) war metaphors (target bank, carpet bombing, scorched earth, fire belt, Philadelphia Axis, Hannibal's plan), (UNRWA, Gaza hospitals, starvation, humanitarian aid) and others. English and Arabic texts can be collected from mainstream media as RT, BBC, CNN, Al-Jazeera and Al-Ghad. A class blog can be created for posting translations, corrections, discussions, and feedback. The students can practice full, summary, and conceptual translation and avoid word-for-word translation. They can watch news stories about the Gaza-Israel war, write a summary translation of it and receive comments and feedback. Beginners can translate short news excerpts (few lines). Students make sure their translations are cohesive, make sense and are easy to read. Students should use Google Translate and artificial intelligence (AI) with caution and should read the same news story in both English and Arabic to get used to the terminology and their equivalents. The instructor serves as a facilitator. Further instructional guidelines and recommendations are given.

A quantitative evaluation of online translators using Hindi web queries

Understanding machine translation (MT) quality is becoming more and more important as MT usage continues to rise in the translation industry. The acceptance of MT output based on their performance and, ultimately, how acceptable the translators actually are, have received relatively less attention so far. MT plays a vital role in CLIR systems and their retrieval efficiency is directly proportional to the translation accuracy of the queries. The varied meanings of words, sentences carrying multiple interpretations, and differing grammatical structures across languages contribute to the complexity of the MT task. The lack of structural constraints and the presence of ambiguity further compound the complications especially in case of web queries. The objective of this work is to assess the accuracy of free online translators in translating Hindi web queries. The accuracy of the translators has been evaluated on various metrics, i.e., BLEU, NIST, METEOR, hLepor, CHRF and GLEU. Our findings indicate that the translation accuracy for longer queries is higher than the shorter ones. Overall Google translator’s performance has been found the best while Systran performs the worst with 42.06% performance difference between the two. The present work intends to help researchers in further evaluating and analyzing the MT systems specially in context of web query translation, ultimately leading to improved translation quality and retrieval accuracy in CLIR.

Can Google Translate be Used in Goals of Care Conversations?

Can Google Translate be Used in Goals of Care Conversations?

Digital resources in teaching Russian to Chinese students outside of the language environment

Introduction. Relations between China and Russia are becoming increasingly close, resulting in a high demand for Russian language specialists in the labor market. The lack of a language environment in Chinese universities is a pressing problem that teachers would like to solve. Therefore, the active implementation of digital technologies will undoubtedly compensate for this deficiency. The purpose of the study is to analyze the use of Chinese internet resources in teaching Russian language in Chinese universities. Materials and Methods. The study involved undergraduate, postgraduate and doctoral students studying in the fields of "Russian as a Foreign Language", "Philology", "Linguistics" and "Russian Literature". A total of 117 respondents from nine higher education institutions in Russia and China. These institutions include Heilongjiang Institute of Foreign Languages, RUDN, Moscow State University, Moscow State Pedagogical University, Institute of China and Modern Asia of the Russian Academy of Sciences, Bauman Moscow State Technical University, Russian State University of Tourism and Service, Inner Mongolia University, and Yili Pedagogical University. The research methods employed in the study were analysis of scientific and scientific-methodological literature, observational (monitoring of online and distance learning), diagnostic (interviews with teachers, student surveys), and statistical analysis. Results. It was found that Chinese students do not know which form of education is most preferable outside of the Russian-speaking environment (35.9% – online, 37.61% – in-person, 38.46% – blended). However, respondents (67%) stated that various online resources provide significant help in learning the Russian language. The most popular programs and platforms were Chinese Dingding and Tencent Meeting, international Microsoft Teams and Zoom. Not all Russian-language dictionaries are available online for Chinese people, which is why the share of translation programs assisting in learning Russian language has increased (over 70%). The most frequently used Chinese dictionary was "Qianyiciba" (62%), and the most popular websites include Google Translate, Yudao Translator, Baidu Translate, and Bilibili. It has been proven that among the main problems hindering the process of learning Russian language in online and distance learning formats, psychological discomfort, lack of communication, increase in independent work, and individualization in learning considering the personal needs of students should be highlighted. Conclusion. The results obtained make it possible to determine the prospects for using digital resources in teaching Russian as a foreign language to Chinese students outside of the language environment.

A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM

Sentiment analysis is an essential task in natural language processing that involves identifying a text’s polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and the Internet, sentiment analysis has become increasingly important in various fields, such as marketing, politics, and customer service. However, sentiment analysis becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose an ensemble model of transformers and a large language model (LLM) that leverages sentiment analysis of foreign languages by translating them into a base language, English. We used four languages, Arabic, Chinese, French, and Italian, and translated them using two neural machine translation models: LibreTranslate and Google Translate. Sentences were then analyzed for sentiment using an ensemble of pre-trained sentiment analysis models: Twitter-Roberta-Base-Sentiment-Latest, bert-base-multilingual-uncased-sentiment, and GPT-3, which is an LLM from OpenAI. Our experimental results showed that the accuracy of sentiment analysis on translated sentences was over 86% using the proposed model, indicating that foreign language sentiment analysis is possible through translation to English, and the proposed ensemble model works better than the independent pre-trained models and LLM.

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.

The use of residual analysis to improve the error rate accuracy of machine translation

The aim of the study is to compare two different approaches to machine translation—statistical and neural—using automatic MT metrics of error rate and residuals. We examined four available online MT systems (statistical Google Translate, neural Google Translate, and two European commission’s MT tools—statistical mt@ec and neural eTranslation) through their products (MT outputs). We propose using residual analysis to improve the accuracy of machine translation error rate. Residuals represent a new approach to comparing the quality of statistical and neural MT outputs. The study provides new insights into evaluating machine translation quality from English and German into Slovak through automatic error rate metrics. In the category of prediction and syntactic-semantic correlativeness, statistical MT showed a significantly higher error rate than neural MT. Conversely, in the category of lexical semantics, neural MT showed a significantly higher error rate than statistical MT. The results indicate that relying solely on the reference when determining MT quality is insufficient. However, when combined with residuals, it offers a more objective view of MT quality and facilitates the comparison of statistical MT and neural MT.