google translate case study

Voice speed

Text translation, source text, translation results, document translation, drag and drop.

Website translation

Enter a URL

Image translation

Machine Translation Accuracy: Google Translate Case Study

Problem definition, task definition, proposed solution.

Machine translation accuracy is a relevant topic, and technology that can successfully convert text from one language into another while keeping the meaning as similar as possible is in high demand. According to Anggaira and Hadi (2017), Google Translate is an inadequate tool for the purpose, mainly when translating less common languages. The software tends to commit numerous morphological and syntax mistakes, and a human language expert should analyze the output to fix errors and correct misinterpreted passages. As such, the problems that the solution attempted to address were the low accuracy and frequent errors committed by the program, particularly when working with less popular languages.

According to Castelvecchi (2016), Google’s algorithms previously did not rely on artificial neural networks, working with traditional methods. Castelvecchi (2016) notes that the tool would scan text word by word, browsing through its database of existing translations and looking for similar situations. The approach is effective at parsing shorter, uncomplicated sentences, but intricate word constructions can severely confuse the machine and cause the structure of the text past the problematic segment to crumble. According to Castelvecchi (2016), Google had to implement an approach that would be able to analyze a sentence by starting from the smallest syntax units and combining them to form meanings.

Machine translation tools accept text in a predefined language as an input and return writing with the same meaning in another requested language as an output. According to Sreelekha, Bhattacharyya, and Malathi (2016), the market for the technology will grow in the future, and translation speed, cost, and quality are all significant factors in the success of an application. Google’s service provides translations adequately quickly and is free for customers, leaving the concern of quality. As almost all machine translations are flawed, the field represents a vital area for improvement if a company wants to obtain an advantage over its competitors.

Expanding the application’s vocabulary and improving its understanding of intricate text constructions contribute to the enhancement of the translation process. According to Li, Zhang, and Zong (2016), unknown words represent a significant challenge for machine translation systems, as they are challenging to handle when the system knows neither the meaning nor the type of the word. However, according to Castelvecchi (2016), Google chose to concentrate on analyzing sentence structure and correctly interpreting word combinations. This choice is likely due to the size of the company’s database described by Castelvecchi (2016), which significantly increases the application’s vocabulary compared to its competitors.

According to Castelvecchi (2016), Google chose to implement translation via neural network analysis to improve the company’s business process, as the approach is effective at improving the quality of tasks that benefit from analytical abilities. According to Luong and Manning (2015), neural machine translation is conceptually simple but can achieve results comparable to state-of-the-art traditional algorithms. The idea requires the creation of a neural network and its education through studying the existing translations compiled by Google. During the supervised learning process, the system becomes capable of predicting and constructing a logic that contains language rules and decision trees for ambiguous situations.

Google will likely optimize the procedures, as the translation tool is expected to assist large numbers of people simultaneously. According to Zhang and Zong (2015), the algorithm can be applied to analyze text mathematically, but also to capture significant amounts of contextual information, improving the speed and accuracy of the translation. When viewed as a black box, the algorithm accepts a text passage and two languages as inputs (the application supports a language recognition option, but the feature can be viewed separately) and produces a matching section of text in the second language as the output.

Google is not the first to implement neural networks in machine translations, but the company may have introduced the approach in a commercial product before its competitors. However, the company has resolved specific issues in an innovative way, such as zero-shot translation (Verma, Jain, Basak, and Saksena, 2018). The concept refers to interpretations of segments for which the application does not have a point of reference and therefore has to “guess” at the correct answer. The ability to perform zero-shot translations significantly enhances the algorithm’s ability to translate text between unpopular languages, where the reference base and training opportunities might be lacking.

Furthermore, the unexplored state of the neural machine translation field, as well as the vast resources at Google’s disposal, allow the company to modify the system with various innovative approaches. Google employs a large number of researchers ( Research, n.d.) that continually investigate potential opportunities for improvement. As such, although the core idea is not innovative, the surrounding details enable a variety of new approaches and ideas.

While the algorithm itself does not concern itself with ethics, Google Translate is subject to two variants of ethical concern. The first one is the ethics of the tool and its parent company with regard to the privacy of its users. According to Kamocki and O’Regan (2016), all the information entered into the application is processed on Google servers, and nothing prevents the company from saving the data and using it later. Furthermore, most users are not aware of the fact or do not pay much attention, which exacerbates the risk.

Good practice regarding the issue would require Google to notify users that their inputs may be collected and analyzed and possibly enable the option to decline such data submissions. Schaub, Balebako, Durity, and Cranor (2015) describe a variety of factors that should be considered when designing privacy notices as well as use cases that match the various uses of Google Translate. Currently, the application keeps its privacy statement on a separate page twice removed from the main one, and the button that lets the user access the policy is small and does not attract significant attention.

Anggaira, A. S., & Hadi, M. S. (2017). Linguistic errors on narrative text translation using Google Translate. Pedagogy: Journal of English Language Teaching, 5 (1), 1-14.

Castelvecchi, D. (2016). Deep learning boosts Google Translate tool. Web.

Kamocki, P., & O’Regan, J. (2016). Privacy issues in online machine translation services – European perspective . Web.

Li, X., Zhang, J., & Zong, C. (2016). Towards zero unknown word in neural machine translation. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (pp. 2852-2858). Palo Alto, CA: AAAI Press.

Luong, M. T., & Manning, C. D. (2015). Stanford neural machine translation systems for spoken language domains . Web.

Research. (n.d.). Web.

Schaub, F., Balebako, R., Durity, A. L., & Cranor, L. F. (2015). A design space for effective privacy notices. In Eleventh Symposium on Usable Privacy and Security (pp. 1-17). Ottawa, Canada: Carleton University.

Sreelekha, S., Bhattacharyya, P., Jha, S. K., & Malathi, D. (2016). A survey report on evolution of machine translation. International Journal of Control Theory and Applications, 9 (33), 233-240.

Verma, M. N., Jain, A., Basak, A., & Saksena, K. B. (2018). Survey and analysis on language translator using neural machine translation. International Research Journal of Engineering and Technology, 5 (4), 3720-3726.

Zhang, J., & Zong, C. (2015). Deep neural networks in machine translation: An overview. IEEE Intelligent Systems, 30 (5), 16-25.

• Mobile Application Services: Digital Credibility in Education
• Descriptive Translation Studies Benefits and Limitations
• "Thick" Translation and Interpretations of Concepts
• Information and Communication Tech for Development
• Global Positioning System: History and Development
• Geographic Information System (GIS) in Network Analysis
• Wood-Based Technology and Manufacturing Processes
• Effective Use of Visual Aids as Objects to Make a Presentation Better
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2022, January 20). Machine Translation Accuracy: Google Translate. https://ivypanda.com/essays/google-translate-case-study/

"Machine Translation Accuracy: Google Translate." IvyPanda , 20 Jan. 2022, ivypanda.com/essays/google-translate-case-study/.

IvyPanda . (2022) 'Machine Translation Accuracy: Google Translate'. 20 January.

IvyPanda . 2022. "Machine Translation Accuracy: Google Translate." January 20, 2022. https://ivypanda.com/essays/google-translate-case-study/.

1. IvyPanda . "Machine Translation Accuracy: Google Translate." January 20, 2022. https://ivypanda.com/essays/google-translate-case-study/.

Bibliography

IvyPanda . "Machine Translation Accuracy: Google Translate." January 20, 2022. https://ivypanda.com/essays/google-translate-case-study/.

Assessing gender bias in machine translation: a case study with Google Translate

• Original Article
• Published: 27 March 2019
• Volume 32 , pages 6363–6381, ( 2020 )

Cite this article

• Marcelo O. R. Prates   ORCID: orcid.org/0000-0002-5576-7060 1 ,
• Pedro H. Avelar 1 &
• Luís C. Lamb 1  

11k Accesses

97 Citations

65 Altmetric

Explore all metrics

Recently there has been a growing concern in academia, industrial research laboratories and the mainstream commercial media about the phenomenon dubbed as machine bias , where trained statistical models—unbeknownst to their creators—grow to reflect controversial societal asymmetries, such as gender or racial bias. A significant number of Artificial Intelligence tools have recently been suggested to be harmfully biased toward some minority, with reports of racist criminal behavior predictors, Apple’s Iphone X failing to differentiate between two distinct Asian people and the now infamous case of Google photos’ mistakenly classifying black people as gorillas. Although a systematic study of such biases can be difficult, we believe that automated translation tools can be exploited through gender neutral languages to yield a window into the phenomenon of gender bias in AI. In this paper, we start with a comprehensive list of job positions from the U.S. Bureau of Labor Statistics (BLS) and used it in order to build sentences in constructions like “He/She is an Engineer” (where “Engineer” is replaced by the job position of interest) in 12 different gender neutral languages such as Hungarian, Chinese, Yoruba, and several others. We translate these sentences into English using the Google Translate API, and collect statistics about the frequency of female, male and gender neutral pronouns in the translated output. We then show that Google Translate exhibits a strong tendency toward male defaults, in particular for fields typically associated to unbalanced gender distribution or stereotypes such as STEM (Science, Technology, Engineering and Mathematics) jobs. We ran these statistics against BLS’ data for the frequency of female participation in each job position, in which we show that Google Translate fails to reproduce a real-world distribution of female workers. In summary, we provide experimental evidence that even if one does not expect in principle a 50:50 pronominal gender distribution, Google Translate yields male defaults much more frequently than what would be expected from demographic data alone. We believe that our study can shed further light on the phenomenon of machine bias and are hopeful that it will ignite a debate about the need to augment current statistical translation tools with debiasing techniques—which can already be found in the scientific literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Gender Bias in Machine Translation Systems

Gender and Age Bias in Commercial Machine Translation

Mitigating Gender Bias in Machine Learning Data Sets

Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias: there’s software used across the country to predict future criminals and it’s biased against blacks. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing . Last visited 2017-12-17

Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arxiv:1409.0473 . Accessed 9 Mar 2019

Bellens E (2018) Google translate est sexiste. https://datanews.levif.be/ict/actualite/google-translate-est-sexiste/article-normal-889277.html?cookie_check=1549374652 . Posted 11 Sep 2018

Boitet C, Blanchon H, Seligman M, Bellynck V (2010) MT on and for the web. In: 2010 International conference on natural language processing and knowledge engineering (NLP-KE), IEEE, pp 1–10

Bolukbasi T, Chang KW, Zou JY, Saligrama V, Kalai AT (2016) Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In: Advances in neural information processing systems 29: annual conference on neural information processing systems 2016, December 5–10. Barcelona, Spain, pp 4349–4357

Boroditsky L, Schmidt LA, Phillips W (2003) Sex, syntax, and semantics. In: Getner D, Goldin-Meadow S (eds) Language in mind: advances in the study of language and thought. MIT Press, Cambridge, pp 61–79

Google Scholar  

Bureau of Labor Statistics (2017) Table 11: employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity, 2017. Labor force statistics from the current population survey, United States Department of Labor, Washington D.C

Carl M, Way A (2003) Recent advances in example-based machine translation, vol 21. Springer, Berlin

Book   MATH   Google Scholar  

Chomsky N (2011) The golden age: a look at the original roots of artificial intelligence, cognitive science, and neuroscience (partial transcript of an interview with N. Chomsky at MIT150 Symposia: Brains, minds and machines symposium). https://chomsky.info/20110616/ . Last visited 26 Dec 2017

Clauburn T (2018) Boffins bash Google Translate for sexism. https://www.theregister.co.uk/2018/09/10/boffins_bash_google_translate_for_sexist_language/ . Posted 10 Sep 2018

Dascal M (1982) Universal language schemes in England and France, 1600–1800 comments on James Knowlson. Studia leibnitiana 14(1):98–109

Diño G (2019) He said, she said: addressing gender in neural machine translation. https://slator.com/technology/he-said-she-said-addressing-gender-in-neural-machine-translation/ . Posted 22 Jan 2019

Dryer MS, Haspelmath M (eds) (2013) WALS online. Max Planck Institute for Evolutionary Anthropology, Leipzig

Firat O, Cho K, Sankaran B, Yarman-Vural FT, Bengio Y (2017) Multi-way, multilingual neural machine translation. Comput Speech Lang 45:236–252. https://doi.org/10.1016/j.csl.2016.10.006

Article   Google Scholar  

Garcia M (2016) Racist in the machine: the disturbing implications of algorithmic bias. World Policy J 33(4):111–117

Google: language support for the neural machine translation model (2017). https://cloud.google.com/translate/docs/languages#languages-nmt . Last visited 19 Mar 2018

Gordin MD (2015) Scientific Babel: how science was done before and after global English. University of Chicago Press, Chicago

Book   Google Scholar  

Hajian S, Bonchi F, Castillo C (2016) Algorithmic bias: from discrimination discovery to fairness-aware data mining. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 2125–2126

Hutchins WJ (1986) Machine translation: past, present, future. Ellis Horwood, Chichester

Johnson M, Schuster M, Le QV, Krikun M, Wu Y, Chen Z, Thorat N, Viégas FB, Wattenberg M, Corrado G, Hughes M, Dean J (2017) Google’s multilingual neural machine translation system: enabling zero-shot translation. TACL 5:339–351

Kay P, Kempton W (1984) What is the Sapir–Whorf hypothesis? Am Anthropol 86(1):65–79

Kelman S (2014) Translate community: help us improve google translate!. https://search.googleblog.com/2014/07/translate-community-help-us-improve.html . Last visited 12 Mar 2018

Kirkpatrick K (2016) Battling algorithmic bias: how do we ensure algorithms treat us fairly? Commun ACM 59(10):16–17

Knebel P (2019) Nós, os robôs e a ética dessa relação. https://www.jornaldocomercio.com/_conteudo/cadernos/empresas_e_negocios/2019/01/665222-nos-os-robos-e-a-etica-dessa-relacao.html . Posted 4 Feb 2019

Koehn P (2009) Statistical machine translation. Cambridge University Press, Cambridge

Koehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E (2007) Moses: open source toolkit for statistical machine translation. In: ACL 2007, Proceedings of the 45th annual meeting of the association for computational linguistics, June 23–30, 2007, Prague, Czech Republic. http://aclweb.org/anthology/P07-2045 . Accessed 9 Mar 2019

Locke WN, Booth AD (1955) Machine translation of languages: fourteen essays. Wiley, New York

MATH   Google Scholar  

Mills KA (2017) ’Racist’ soap dispenser refuses to help dark-skinned man wash his hands—but Twitter blames ’technology’. http://www.mirror.co.uk/news/world-news/racist-soap-dispenser-refuses-help-11004385 . Last visited 17 Dec 2017

Moss-Racusin CA, Molenda AK, Cramer CR (2015) Can evidence impact attitudes? Public reactions to evidence of gender bias in stem fields. Psychol Women Q 39(2):194–209

Norvig P (2017) On Chomsky and the two cultures of statistical learning. http://norvig.com/chomsky.html . Last visited 17 Dec 2017

Olson P (2018) The algorithm that helped Google Translate become sexist. https://www.forbes.com/sites/parmyolson/2018/02/15/the-algorithm-that-helped-google-translate-become-sexist/#1c1122c27daa . Last visited 12 Mar 2018

Papenfuss M (2017) Woman in China says colleague’s face was able to unlock her iPhone X. http://www.huffpostbrasil.com/entry/iphone-face-recognition-double_us_5a332cbce4b0ff955ad17d50 . Last visited 17 Dec 2017

Rixecker K (2018) Google Translate verstärkt sexistische vorurteile. https://t3n.de/news/google-translate-verstaerkt-sexistische-vorurteile-1109449/ . Posted 11 Sep 2018

Santacreu-Vasut E, Shoham A, Gay V (2013) Do female/male distinctions in language matter? Evidence from gender political quotas. Appl Econ Lett 20(5):495–498

Schiebinger L (2014) Scientific research must take gender into account. Nature 507(7490):9

Shankland S (2017) Google Translate now serves 200 million people daily. https://www.cnet.com/news/google-translate-now-serves-200-million-people-daily/ . Last visited 12 Mar 2018

Thompson AJ (2014) Linguistic relativity: can gendered languages predict sexist attitudes?. Linguistics Department, Montclair State University, Montclair

Wang Y, Kosinski M (2018) Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. J Personal Soc Psychol 114(2):246–257

Weaver W (1955) Translation. In: Locke WN, Booth AD (eds) Machine translation of languages, vol 14. Technology Press, MIT, Cambridge, pp 15–23. http://www.mt-archive.info/Weaver-1949.pdf . Last visited 17 Dec 2017

Women’s Bureau – United States Department of Labor (2017) Traditional and nontraditional occupations. https://www.dol.gov/wb/stats/nontra_traditional_occupations.htm . Last visited 30 May 2018

Download references

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

Author information

Authors and affiliations.

Federal University of Rio Grande do Sul, Porto Alegre, Brazil

Marcelo O. R. Prates, Pedro H. Avelar & Luís C. Lamb

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Marcelo O. R. Prates .

Ethics declarations

Conflict of interest.

All authors declare that they have no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Prates, M.O.R., Avelar, P.H. & Lamb, L.C. Assessing gender bias in machine translation: a case study with Google Translate. Neural Comput & Applic 32 , 6363–6381 (2020). https://doi.org/10.1007/s00521-019-04144-6

Download citation

Received : 19 October 2018

Accepted : 09 March 2019

Published : 27 March 2019

Issue Date : May 2020

DOI : https://doi.org/10.1007/s00521-019-04144-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Machine bias
• Gender bias
• Machine learning
• Machine translation
• Find a journal
• Publish with us
• Track your research

Marcelo Prates

CS PhD from UFRGS, Data Scientist @ Dataside, Generative Artist

• Porto Alegre
• Google Scholar

Assessing Gender Bias in Machine Translation: a Case Study with Google Translate

Published in Neural Computing and Applications , 2019

Recommended citation: Prates, M. O., Avelar, P. H., & Lamb, L. C. (2018). Assessing gender bias in machine translation: a case study with Google Translate. Neural Computing and Applications, 1-19. https://link.springer.com/article/10.1007/s00521-019-04144-6

Recently there has been a growing concern in academia, industrial research laboratories and the mainstream commercial media about the phenomenon dubbed as machine bias, where trained statistical models—unbeknownst to their creators—grow to reflect controversial societal asymmetries, such as gender or racial bias. A significant number of Artificial Intelligence tools have recently been suggested to be harmfully biased toward some minority, with reports of racist criminal behavior predictors, Apple’s Iphone X failing to differentiate between two distinct Asian people and the now infamous case of Google photos’ mistakenly classifying black people as gorillas. Although a systematic study of such biases can be difficult, we believe that automated translation tools can be exploited through gender neutral languages to yield a window into the phenomenon of gender bias in AI. In this paper, we start with a comprehensive list of job positions from the U.S. Bureau of Labor Statistics (BLS) and used it in order to build sentences in constructions like “He/She is an Engineer” (where “Engineer” is replaced by the job position of interest) in 12 different gender neutral languages such as Hungarian, Chinese, Yoruba, and several others. We translate these sentences into English using the Google Translate API, and collect statistics about the frequency of female, male and gender neutral pronouns in the translated output. We then show that Google Translate exhibits a strong tendency toward male defaults, in particular for fields typically associated to unbalanced gender distribution or stereotypes such as STEM (Science, Technology, Engineering and Mathematics) jobs. We ran these statistics against BLS’ data for the frequency of female participation in each job position, in which we show that Google Translate fails to reproduce a real-world distribution of female workers. In summary, we provide experimental evidence that even if one does not expect in principle a 50:50 pronominal gender distribution, Google Translate yields male defaults much more frequently than what would be expected from demographic data alone. We believe that our study can shed further light on the phenomenon of machine bias and are hopeful that it will ignite a debate about the need to augment current statistical translation tools with debiasing techniques—which can already be found in the scientific literature.

Download paper here

The Pros and Cons of Using Google Translate for Websites

Want to use google translate on your website here's how the pros and cons of using the service affect your business..

In today's globalized world, having a multilingual website is essential for businesses to reach a wider audience. And when it comes to translation tools, Google Translate often comes to mind. But is it really the best choice for your business website? In this blog, we will dive deep into the pros and cons of using Google Translate . We'll start by understanding how Google Translate works, including the role of neural networks in enhancing translation quality. Then, we'll explore the positive aspects, such as quick translations and universal accessibility. However, we won't shy away from discussing the drawbacks too, like inaccurate translations and lack of contextual understanding. We'll also analyze case studies of companies that have struggled with Google Translate and explore why human touch in translations matters. Finally, we'll provide alternatives to consider and guide you in choosing the right translation method for your website. Don't make a hasty decision; read on to make an informed choice for your business.

Understanding Google Translate: An Overview

Google Translate is a widely used translation tool for websites. It offers several advantages, such as instant translation of content into multiple languages, making it a cost-effective solution for small businesses and individuals. Implementation is also easy with a simple code snippet. However, there are some cons to consider. The accuracy and quality of translations may vary, which can be a disadvantage when it comes to professional or nuanced translations.

The Mechanism Behind Google Translate

Google Translate operates by utilizing machine learning algorithms to analyze and translate text from one language to another. With the ability to handle a wide range of languages, it provides accessibility to users around the world. However, the accuracy of translations can vary depending on the complexity of the text and language pair. Google Translate may struggle with idiomatic expressions, cultural nuances, and technical terminology. It is crucial to review and edit translations for accuracy and clarity. Yet, for small businesses or individuals in need of basic translation services, Google Translate can be a cost-effective solution.

The Role of Neural Networks in Google Translate

Google Translate harnesses the power of neural networks to enhance translation accuracy and quality. By utilizing these advanced algorithms, Google Translate is able to understand the context and nuances of different languages, resulting in significant improvements in translation speed and accuracy. However, it's important to acknowledge that there are limitations to relying solely on Google Translate. Potential inaccuracies and the loss of cultural nuances in translations should be taken into consideration before using this tool for website translations.

How Neural Networks Enhance Translation Quality

Neural networks, a form of artificial intelligence, play a crucial role in enhancing the translation quality of Google Translate. By analyzing vast amounts of data across multiple languages, neural networks can learn patterns and improve the accuracy of translations. This technology enables Google Translate to understand the nuances and context of language, resulting in more precise and reliable translations. Neural networks can also handle complex sentence structures and idiomatic expressions, further enhancing the translation quality for websites. However, it is important to note that Google Translate is not flawless and may still produce inaccuracies, particularly for less common languages or specialized content. Website owners should review and edit machine-translated content to maintain professionalism and ensure accuracy.

The Positive Aspects of Google Translate for Websites

Google Translate is an invaluable tool for websites, offering free and easy translation services in multiple languages. This allows businesses to reach a broader audience by making their content accessible to non-English speakers. Regular updates to Google Translate's algorithms ensure improved translation accuracy over time. With a wide range of language options, businesses can effectively cater to specific target markets.

Quick Translation: A Key Advantage

Quick translation is one of the main advantages of using Google Translate for websites. With this feature, web pages can be instantly translated into multiple languages, allowing users from different countries to access and understand the content. This not only expands a website's reach but also increases its global audience. Furthermore, quick translation saves time and effort as it eliminates the need for manual translation or hiring professional translators. Website updates and new content can be efficiently translated, ensuring that all users have access to the latest information.

Accessibility and Universal Usage of Google Translate

Google Translate allows websites to be accessible to a global audience by providing translations in multiple languages. Being a free and easy-to-use tool, it can be implemented on websites without much technical knowledge. Google Translate automatically detects the user's language and translates the website content accordingly, delivering a seamless experience for non-native speakers. With machine learning algorithms, the tool constantly improves its accuracy and translation quality over time. It offers a wide range of supported languages, making it suitable for websites targeting diverse international audiences.

The Drawbacks of Using Google Translate for Business Websites

Using Google Translate for business websites comes with several drawbacks that can impact the overall user experience and credibility of the website. One of the main concerns is the potential for inaccurate translations, especially for complex or industry-specific content. Additionally, Google Translate lacks context, leading to mistranslations and misinterpretations. The poor grammar and syntax in the translations can make the website appear unprofessional. Customization options are limited, making it difficult to match the brand's tone and style. Ultimately, relying solely on machine translation may lead to a loss of trust from potential customers.

The Risk of Inaccurate Translations

Google Translate relies on machine translation technology, which may not always provide accurate translations for complex or nuanced content. Mistranslations and errors in the translated content can confuse or mislead website visitors, potentially harming a business's reputation and credibility. Additionally, Google Translate may struggle to capture the cultural context and tone of the original content, resulting in translations that feel unnatural or inappropriate. By relying solely on Google Translate, businesses may miss out on the opportunity to provide a more personalized and professional translation service for their international audience.

Lack of Contextual Understanding and Localization

Google Translate, while a convenient translation tool, may not always accurately capture the nuances and context of the original text, leading to potential mistranslations. This can be particularly problematic when the translation is not culturally appropriate or sensitive to the target audience. Additionally, Google Translate cannot account for industry-specific terminology or jargon, which may result in inaccurate translations for specialized websites. Automated translations lack the human touch and professional editing necessary for high-quality translations, potentially creating a negative impression of your business if the translated content contains errors or is of poor quality.

Case Studies: Companies Struggling with Google Translate

Case studies of companies that have faced challenges with using Google Translate for website localization serve as valuable insights into the limitations and potential risks of relying solely on machine translation. These case studies highlight the importance of accurate and culturally appropriate translations and shed light on the disadvantages of automated translation. By examining these real-world examples, businesses can learn best practices for using Google Translate effectively and explore alternative solutions to achieve high-quality, linguistically and culturally accurate translations.

Lessons Learned from Translation Failures

While Google Translate can offer a quick and cost-effective solution for website translation, it is crucial to be aware of its limitations. Machine translation lacks the ability to understand context, cultural nuances, and idiomatic expressions, often resulting in inaccurate and nonsensical translations. Relying solely on Google Translate can lead to misunderstandings and damage a company's reputation. When deciding whether to use Google Translate or professional human translators, it is essential to consider the target audience, content complexity, and the importance of accuracy.

Why Human Touch in Translations Matters?

The human touch in translations is crucial for accuracy and cultural sensitivity. Translators understand context, nuances, and idiomatic expressions, adapting the translation for the target audience while maintaining the original intent. Professional translators ensure high-quality translations devoid of errors or misinterpretations.

The Importance of Professional Translators

Professional translators play a crucial role in ensuring accurate and meaningful translations for websites. While Google Translate offers convenience, professional translators have a deep understanding of language nuances and cultural differences, allowing them to provide high-quality translations. Unlike machine translation, professional translators can adapt the translation to the target audience, tailoring it for specific regions or cultural contexts. They also possess the ability to maintain the tone and style of the original content, enhancing user experience. In recent years, businesses have recognized the value of professional translation services in overcoming language barriers and delivering accurate and localized website content.

Alternatives to Google Translate for Businesses

While Google Translate offers convenience and cost-effectiveness for website translation, there are limitations to consider. Inaccurate translations and lack of cultural sensitivity can pose challenges. Businesses have alternative options, such as professional translation services or specialized website translation plugins. Factors like quality, speed, and cost should be considered when choosing the best solution. Successful implementation of alternative methods has resulted in improved translation quality and user experience.

Considering Professional Translation Services

When it comes to translating website content, Google Translate can offer convenience and cost-effectiveness. However, there are some drawbacks. Machine translation tools like Google Translate may not always produce accurate results and can lead to errors or mistranslations. On the other hand, professional translation services provide higher quality and more accurate translations, though they can be more expensive. Professional translators have expertise in specific industries, ensuring culturally appropriate and meaningful translations that reflect the intended message. Using professional translation services can also save businesses time and effort by eliminating the need for manual review and correction of machine-translated content.

The Power of Localization Software

Localization software is a powerful tool for website translation. While Google Translate offers convenience, localization software goes beyond machine translation by considering cultural nuances and targeting specific markets. With localization software, businesses can ensure a higher quality translation that resonates with the target audience. Although it may require more time and resources upfront, the investment can result in an effective and culturally appropriate website translation. It's crucial for businesses to weigh the pros and cons of using Google Translate versus investing in localization software.

How to Choose the Right Translation Method for Your Website?

Consider factors like translation accuracy, cost, scalability, control, and customization when choosing a translation method. Also, think about your website's specific needs and target audience requirements.

Is Relying Solely on Google Translate a Wise Business Decision?

Relying solely on Google Translate may not be the best choice for businesses. Machine translation can be inaccurate, especially for complex content. Investing in professional translation services ensures higher quality and accurate translations, enhancing your brand's credibility and communication with customers.

In conclusion, while Google Translate can be a convenient tool for basic translations, it has its limitations when it comes to accurately convey the context and nuances of language. For businesses, relying solely on Google Translate for website translations can lead to inaccurate translations and misunderstandings with customers. It is important to consider the importance of human touch in translations and the value of professional translators who understand the cultural nuances and context of your target audience. Alternatives such as professional translation services and localization software provide more reliable and accurate options for businesses looking to expand their global reach. To learn more about choosing the right translation method for your website, download our guide now.

I am a young writer who loves writing about the restaurant industry. I spend a lot of my time researching about how restaurants work and what makes a restaurant successful. I am here to help you to build a succesful food business.

Sign up for our newsletter

Recent articles.

Help | Advanced Search

Computer Science > Computers and Society

Title: assessing gender bias in machine translation -- a case study with google translate.

Abstract: Recently there has been a growing concern about machine bias, where trained statistical models grow to reflect controversial societal asymmetries, such as gender or racial bias. A significant number of AI tools have recently been suggested to be harmfully biased towards some minority, with reports of racist criminal behavior predictors, Iphone X failing to differentiate between two Asian people and Google photos' mistakenly classifying black people as gorillas. Although a systematic study of such biases can be difficult, we believe that automated translation tools can be exploited through gender neutral languages to yield a window into the phenomenon of gender bias in AI. In this paper, we start with a comprehensive list of job positions from the U.S. Bureau of Labor Statistics (BLS) and used it to build sentences in constructions like "He/She is an Engineer" in 12 different gender neutral languages such as Hungarian, Chinese, Yoruba, and several others. We translate these sentences into English using the Google Translate API, and collect statistics about the frequency of female, male and gender-neutral pronouns in the translated output. We show that GT exhibits a strong tendency towards male defaults, in particular for fields linked to unbalanced gender distribution such as STEM jobs. We ran these statistics against BLS' data for the frequency of female participation in each job position, showing that GT fails to reproduce a real-world distribution of female workers. We provide experimental evidence that even if one does not expect in principle a 50:50 pronominal gender distribution, GT yields male defaults much more frequently than what would be expected from demographic data alone. We are hopeful that this work will ignite a debate about the need to augment current statistical translation tools with debiasing techniques which can already be found in the scientific literature.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

1 blog link

Dblp - cs bibliography, bibtex formatted citation.

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Google Translate as an Alternative Tool for Assisting Students in Doing Translation : A Case Study at Universitas Negeri Jakarta, Indonesia

BAHTERA : Jurnal Pendidikan Bahasa dan Sastra

The paper examines the use of Google Translate as an Alternative tool for assisting students at Universitas Negeri Jakarta, Indonesia to translate and develop their knowledge and skills in doing Translation. The participants of the study were 36 students at the Applied Linguistic of Doctoral Program at Universitas Negeri Jakarta who had registered of the year 2017. Based on literature review, analysis of the collecting data, and an assessment of the course content and activities inside and outside the learning process, the findings suggest that most Applied Linguistic of Doctoral Program students at Universitas Negeri Jakarta recognize Google Translate as an Alternative tool for doing references book translation. In fact, some students reported that they could optimally ,benefit from self-learning if they were assisted to use Google Translate effectively. Moreover, using Google Translate for doing classroom tasks and reference books translation encourage students to study independ...

Related Papers

JELT: Journal of English Language Teaching and Linguistics

Helena M . Rijoly

This research aimed to discover how students used Google Translate (GT), their refinement efforts, and their perceptions towards using GT in The Translation Class. The research participants were students in The Translation Class of the English Education Study Program at Pattimura University in the academic year 2022/2023. An explanatory sequential mixed method design was used in this study. The qualitative data were obtained from observation and interview and analyzed using thematic analysis. Meanwhile, the quantitative data was obtained from a questionnaire and analyzed using descriptive statistics. The results showed that students mostly used GT to facilitate translation tasks, especially for assignments, learning materials, and new vocabulary. In the classroom, GT was allowed for assignments but prohibited during exams, while students were free to use it at home. Students usually try to translate independently before using GT. They recognized the limitations of GT and made efforts to confirm and improve the translation. Despite these shortcomings, students maintained positive perceptions regarding the usefulness of GT in the Translation Classroom. These findings highlight differences in students' attitudes and perceptions towards GT and its role in translation classrooms.

Aideliannisa Dalimunthe

Google translate is one machine translators who can translate from one language, another, for example from Indonesian to English. This is usually often used by both students and student academics. This study aims to determine the extent Google translate can help both students academics and students in learning English, as Google translate is one that is frequently used machine translators in terms of translation. The translation engine can translated either per word, phrase, clause, sentence and even paragraph text, but there are some obstacles that are in the use of machine translation, the first to be connected to the internet with a fast enough network, then not all of the messages or the results contained in the source text can be translated appropriately by the engine or Google translate. Not exactly the result of the target language is usually due to the use of the source language that is too market or use mixed languages.

Research in Higher Education Journal

Eszter Tarsoly

We propose ways of incorporating Google Translate into the teaching of Finnish and Hungarian in a higher education setting at different skill levels. The task types tested in our study were: analytical tasks (dictionary-like exercise, word-building, part-of-word identification), discovery method tasks (elicitation, problem solving), and awareness raising tasks (error correction, text-level error analysis, guided essay writing in the target language). Students were interviewed about their experience as users of Google Translate and the usefulness of the exercises conducted in class. In line with the principles of action research, the survey results enabled the practitioners to reflect on and improve the teaching of two morphologically complex languages, Finnish and Hungarian, and optimise the ways in which Google Translate is used in the language classroom. With the development of their Finnish and Hungarian language skills, students become more critical, and more competent, users of...

KLASIKAL : JOURNAL OF EDUCATION, LANGUAGE TEACHING AND SCIENCE

Salasiah Ammade

This Google translation (GT) is one of main importance tool for students in assisting them in translating English words or sentences. Many researches have investigated its importance to aid students in learning, but still limit research focuses on how GT as English learning tool assist non-English department student. The current research tries to fill the gap by researching how Google translates support non-English department students in learning English viewed from students’ lens. This research used qualitative research, using case study. The participants of this research consist of 10 non-English students in Faculty of Teacher Training and Education of Universitas Muhammadiyah Parepare. The researcher conducted interview via WhatsApp voice note to obtain the data from the participants. The data then analyzed using Miles & Huberman pattern. The result of this research showed that GR is a good application to support non-English students in learning English. It was found that the app...

International Journal of Emerging Technologies in Learning (iJET)

Phúc Huân Huỳnh

Machine learning has globally become a trend in most educational settings. This study aims to explore students’ perceptions when using Google Translate (GT) to support their learning as well as their problems and solutions from GT. With the participation of 250 university students at a private educational institution, a 5-point Likert-scale questionnaire and a semi-structured interview were employed to examine how students perceived the use of GT in their learning process. The findings revealed that practically students had positive perceptions on GT’s use in learning. Several major problems were recorded when they used GT, and some recommended solutions were also considered for improvement. Specifically, students utilized GT as a learning tool, particularly for language study, because of its useful features such as multi-language translation, time saving, ease of use, and improving pronunciation. Although Google Translate has a number of advantages for students, several problems su...

International Journal of Scientific and Research Publications (IJSRP)

Roswani Siregar

Language Literacy: Journal of Linguistics, Literature, and Language Teaching

Rudy Sofyan

This paper is concerned with the problems in the application of Google Translate as a translation tool. The discussion focuses on the identification of the problems faced by the translators. This research was conducted by using the descriptive qualitative method with a case study approach. Some theories supporting this research were proposed by Munday, Imre, and also Ghasemi and Hasemian. The data sources in this research were taken from the questionnaires given to students of English Literature Department of USU. From the analysis, it was found that the biggest problems often faced by the students of English Literature Department of USU were the inaccuracy and mismatch of the meaning translated which reached 31%, followed by the inaccuracy of the language structure in the translation result which reached 30%. On the other hand, the findings also showed that the best solution to deal with these problems was to make self-corrections and check the meaning of some suspected words in th...

The using of Google Translate in translation class has been an issue whether it has to be avoided or not. The problem of this research was different perceptions among English students about the using of Google Translate in translation class. The purpose of this research was to find out students’ perception on the using of Google Translate in translation class based on cognitive, affective, and connative aspect. This research used a quantitative approach witha survey design. The population of this study were the students who had taken translation class at the fourth semester of English Departement in academic year 2021/2022 consisting of 37 students from 4 classes. The researcher used total sampling because the population are less than 100 students. Instrument of this research was questionnaire which consisted of 30 items. The researcher did the validity instrument through three experts. In analyzing the data, the researcher used quantitative descriptive analysis. Total percentage of...

Alta van Rensburg , Susan Lotz

RELATED PAPERS

Journal of Languages and Translation (JLT), vol. 10, Issue 2, pp. 117-139, April 2023 https://jltmin.journals.ekb.eg

Pierre Larcher

Fish Physiology and Biochemistry

Eddie Deane

Revista Brasileira de Geociências

yopie kurniawan

Revista Iberoamericana de Tecnología en Educación y Educación en Tecnología

Gladys Gorga

Journal of Transportation Technologies

Jamal Raiyn

J. Univers. Comput. Sci.

Muhammad Fahad

Research, Society and Development

Eveline Aidar

Silvia Lilian Ferro

Journal of Abnormal Child Psychology

jessica martinez

Annals of Plastic Surgery

Julio Hochberg

International Surgery Journal

Sarath Sistla

Journal of Pharmaceutical Health Services Research

Arul kumaran

Journal of Molecular Structure

The Journal of Applied …

Jesus Tanguma

Richard L Kiesling

Toansakul Santiboon

Procedia Structural Integrity

Section 4: Clinical pharmacy services

Anita Weidmann

Journal of African Studies

Jean-Claude Maswana

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Money Report

DeepL, a European rival to Google Translate, rides AI hype to a $2 billion valuation

By ryan browne,cnbc • published may 22, 2024 • updated on may 22, 2024 at 11:26 am.

• DeepL, an AI-powered translation tool competing with Google Translate, said Wednesday that it has raised $300 million in a round led by Index Ventures.
• The round values DeepL at $2 billion, double what it was worth when it last raised capital in January 2023.
• The company said its fresh funding round would give it financial ammunition to grow and expand in key strategic markets such as the U.S.

PARIS — DeepL, an artificial intelligence-powered translation platform, said Wednesday that it has raised $300 million in fresh funding, in a sign investors are still willing to invest major sums into the AI space. 

24/7 San Diego news stream: Watch NBC 7 free wherever you are

The deal, led by venture capital firm Index Ventures, boosts DeepL's valuation to $2 billion.

ICONIQ Growth and Teachers' Venture Growth came on board as new investors, while existing investors IVP, Atomico and WiL also participated.

Get San Diego local news, weather forecasts, sports and lifestyle stories to your inbox. Sign up for NBC San Diego newsletters.

At $2 billion, DeepL is now worth double what it was in its previous round in January 2023, when it raised $100 million from investors at a $1 billion valuation. 

Launched in 2017 by founder and CEO Jaroslaw "Jarek" Kutylowski, DeepL is a competitor to Google Translate.

Speaking with CNBC on Wednesday following news of the investment, Kutylowski said he is "super confident" about the firm's $2 billion valuation tag and felt it was "pretty moderate."

Oil prices bounce back after three-day decline but still on pace for weekly loss

Alibaba bets on AI to fuel cloud growth as it expands globally to catch up with U.S. tech giants

"Obviously we are at the private stage," Kutylowski told CNBC via a phone interview. "Multiples will be larger than [they are] for publicly traded companies at the later stage."

"Nevertheless, I'm trying to keep it all sensible," he added.

Kutylowski said DeepL's latest fundraising round consisted of a mix of primary investments into the business, as well as a secondary share sale by some early investors, including b2venture, a Swiss venture fund.

"That's down to the fact we don't need those extra levels of capital," he said. "At the same time, we want to have this very strong backing from these late-stage folks."

DeepL offers translations between 32 different languages including English, German, French, Spanish, Italian, Polish and Dutch.

Going forward, Kutylowski said the focus is on expanding and growing in key strategic markets such as the U.S., as well as investing in research and development.

The U.S. is now DeepL's third-largest market. DeepL opened its first U.S. office in January.

DeepL is also looking to add support for more Asian languages over the next year, and Kutylowski noted that Asia is "a very strong market for us."

DeepL's long-term vision, he said, is to become more of a productivity tool for enterprise firms looking to use the company's tools to take tedious manual work out of day-to-day company communications.

"We're really expanding our product toward being able to support these bigger enterprise customers," Kutylowski said.

"For us, the vision here is really to be able to create this kind of language system for our customers. They have this control of the language that their employees are speaking … [and] sending out lists of emails to their employees."

The company has ramped up its focus on selling into enterprise over the past few years and now counts customers including Zendesk, Nikkei, Coursera and Deutsche Bahn.

Also on CNBC

• Nvidia reports first-quarter earnings after the bell
• Amazon plans to give Alexa an AI overhaul — and a monthly subscription price
• After a big hack, Microsoft is changing how it pays top executives

This article tagged under:

• Skip to main content
• Keyboard shortcuts for audio player

Weekend Edition Sunday

• Latest Show

Sunday Puzzle

• Corrections

Listen to the lead story from this episode.

Politics chat: Biden and Trump pin their hopes on debate to give them an edge

by  Tamara Keith ,  Mara Liasson

Meet the woman who escaped two conflicts — as a Palestinian refugee, then in Ukraine

by  Tamara Keith ,  Eleana Tworek

Biden will address the commencement ceremony at Morehouse College. Protests are expected

by  Tamara Keith ,  Stephen Fowler

These teens were missing too much school. Here's what it took to get them back

by  Leigh Paterson ,  Elizabeth Miller

Plant-based restaurants are adding beef. Does the climate math add up?

by  Tamara Keith ,  Julia Simon

An iconic chocolate factory shuts shop in Chicago

by  Michael Puente

Sunday Puzzle NPR hide caption

Sunday Puzzle: Complete the compound with these animal connections!

by  Will Shortz

Tree seeds that flew around the moon are now being planted across the U.S.

by  William Joseph Hudson

Music Interviews

Sudanese musician sinkane on his new album 'we belong'.

by  Tamara Keith

The Supreme Court ruled to protect the CFPB. Here's why it matters for your money

Middle east, anger at netanyahu cuts through a somber tel aviv rally to bring home the hostages.

by  Hadeel Al-Shalchi

Trump addresses NRA's annual meeting, urges them to vote

by  Tamara Keith ,  Caroline Love

A London court will rule on Julian Assange's extradition to the U.S.

by  Tamara Keith ,  Willem Marx

Rock icon or a victim of exploitation? Examining Amy Winehouse's legacy

by  Tamara Keith ,  Stephen Thompson

Scientists at Berkeley develop a tool to help cities measure carbon emissions

by  Kevin Stark / KQED

Furiosa makes a splash at the 2024 Cannes Film Festival

Environment, on a trail in the adirondack mountains, runners appreciate the spring season.

by  Emily Russell, NCPR

Author Interviews

Hold on to your wishes — there's a 'spider in the well'.

by  Tamara Keith ,  Samantha Balaban ,  Melissa Gray

Searching for a song you heard between stories? We've retired music buttons on these pages. Learn more here.