From “the three Rs” to machine translation: A new kind of literacy for the digital age

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Posted: 
October 7, 2019
Written by: Lynne Bowker

Originally, the concept of literacy was tied to reading and writing (and ’rithmetic!), but as our society continues to evolve, we need to include new types of literacy. For instance, in this era of “fake news,” media literacy is emerging as a critical skill. Similarly, as technology takes on a greater and greater role in our lives, computer literacy and other forms of digital literacy are becoming increasingly essential.

One type of technology that has gained prominence in recent years is machine translation. Who today hasn’t used a free online machine translation tool? On the surface, it seems pretty straightforward. Open the tool, copy and paste your text, choose your languages, and click the “Translate” button. Easy, right?

Be aware of the pitfalls

Using the technology may be easy, but using it critically requires a bit more effort. Have you ever thought about what happens to the text that you paste into the tool? Maybe you think it just disappears once you close the window? (Spoiler alert: it doesn’t.) Confidentiality and privacy are some issues worth thinking about before choosing to use an online machine translation system.

And how reliable are the translations that come out? The most recent approach to machine translation (known as neural machine translation) uses artificial intelligence (AI) and a technique known as machine learning. Essentially, the computer program is fed with millions of words of texts and their translations as examples, and it uses this “training data” to learn how to translate new texts. So what’s the problem? Well, depending on the texts that are used for training, the machine translation system might learn some inappropriate things. For instance, there are reports of neural machine translation systems that have produced texts containing different types of bias, such as gender bias or racial bias. It’s important to be aware of such possibilities rather than simply using these systems unthinkingly.

There may be some applications of machine translation that are more suitable than others. If you’re looking for help just to get the gist of a text written in another language for your own personal use, machine translation could be just the ticket. But if you need a high-quality text that you’re planning to distribute, then the chances are good that the machine translation output will need to be reviewed and improved. In such cases, it’s better to treat machine translation as a tool that can help with translation, rather than as one to do translation. Human intervention (before or after the machine translation phase) can make a huge difference to the eventual quality of the text. So while you as a user can’t control for all the potential problems associated with machine translation, you can control more than you might think. Learning how to prepare texts in a machine-translation-friendly way can improve the usability of the translated output.

Give credit where credit is due

Another issue that most users don’t think about is the fact that there are human beings behind the machine too. The 2016 movie “Hidden Figures” shed light on the vital contributions of African American women who worked as mathematicians for NASA and who were instrumental in sending American astronauts into space. Well, machine translation has its own set of “hidden figures.” Even the AI-based machine translation systems wouldn’t be able to function without the contributions of tens of thousands of professional translators. So there’s a bit of an ethical issue to confront here: professional translators are not getting appropriate recognition for their work because all the credit is going to the machine. It’s as though the translation is happening by “magic.” It’s quite ironic that people think that machine translation systems are going to eliminate the need for language professionals, when in reality these tools depend heavily on the contribution of language professionals for their functioning.

Enter “machine translation literacy”

All of these elements, such as thinking about whether, when, why, and how to use machine translation, are part of what I term “machine translation literacy.” It basically comes down to being an informed and critical user of this technology, rather than being someone who just copies, pastes and clicks.

It’s clear that machine translation tools are not going away any time soon, and as an educator, I find myself thinking more and more often about how best to help technology users in language professions and in society at large to become more informed users of machine translation and other translation technologies. Do you have any tips, tricks or other suggestions to share about elements that you think could be usefully incorporated into a machine translation literacy training program? If so, I’d love to hear your ideas!

Disclaimer

The opinions expressed in posts and comments published on the Our Languages blog are solely those of the authors and commenters and do not necessarily reflect the views of the Language Portal of Canada.

About the author

Lynne Bowker

Lynne Bowker holds a PhD in language engineering from the University of Manchester Institute of Science and Technology, in England. She is a full professor at the School of Translation and Interpretation at the University of Ottawa and a certified French-English translator with the Association of Translators and Interpreters of Ontario. From July to December 2019, she is the researcher-in-residence at Concordia University Library, in Montreal, where she is promoting increased machine translation literacy by delivering a series of workshops to students and faculty.

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