Since I wrote my book of language-learning / teaching activities that involve the use of the learners’ own language (Kerr, 2014), one significant change has taken place. Some of these activities focused on machine translation tools, like Google Translate. The main concern at the time was the lack of reliability of these tools, and many teachers were strongly opposed to their students using them. It was easy to find examples of bad translation and to laugh at them. My favourite was an image of a crowd welcoming Pope Francis to Cuba, where a banner saying ‘Welcome Potato’ was supposedly a mistranslation of the Spanish ‘papa’, which can mean both ‘pope’ and ‘potato’. It’s a pity the image was Photoshopped.
My approach, feeling that it was impracticable and counter-productive to ban Google Translate altogether, was to exploit the poor quality of many of the translations as a way of training learners to use them more critically and more effectively. But, in the intervening years, the accuracy of online translation has much improved. One study (Aiken, 2019) found that Google Translate had improved by 34% over an 8-year period, although there were still significant differences in the accuracy of particular language pairings. Improvements will continue, and there are new services like DeepL Translator, which was launched in 2017, and, in my view, generally outperforms Google Translate, although fewer language pairings are available. 100% translation accuracy (if such a thing actually exists) may never be achievable, but for some kinds of texts with some language pairings, we are effectively there.
Training in using online translation is, however, still needed for some language pairings. There are two good ways of starting this.
1 Take a text in the learners’ L1 and machine-translate it into English. Highlight the errors and give it to the learners along with the original and a list of common error types (see below). The learners work together, looking at the highlighted errors and attempting to match them to one of the error types on the list.
2 Take a text in English and machine-translate it into the learners’ L1. The learners work together, first identifying and highlighting the errors they find, then comparing the translation with the original and attempting to identify the reasons for the error having happened.
At the time that I wrote this book, I would have advised against using Google Translate as a dictionary to look up single words, on the grounds that (1) the tool worked better the more context / co-text it had, and (2) there were usually better bilingual dictionaries available. My position has shifted somewhat, primarily because the features that Google Translate now offers have improved. There’s a video by Russell Stannard, called ‘Using Google Translate in Language Teaching -Tips and Ideas’,where Russell basically uses the software as a dictionary tool, and enthuses about the possibilities for pronunciation and listening work, for using the ‘favourites’ feature, and for exporting, via a spreadsheet, wordlists that are selected so they can be used with a spaced-repetition memory trainer.
You can find more ideas for using Google Translate as a pronunciation training tool in Minh Trang (2019).
One of the most common uses of machine translation by learners is undoubtedly in the production of written work. One recent piece of research (Tsai, 2019) came to the less than surprising conclusion that learners produced better drafts when doing so, and were happy to use it. Whether or not more learning took place when machine translation was used is another matter. O’Neill (2019) came to a similar conclusion, but found that students performed better with prior training. This training consisted of two 20-minutes sessions, where students tested the tool with examples before reviewing its strengths and weaknesses. More ideas for machine translation literacy training can be found in Bowker (2020).
I’d like to suggest a couple of further activities where Google Translate or DeepL can be used in the preparation of activities. In both cases, I’ll illustrate with the short original text from a newspaper (Der Standard) below:
Eine Passage in der neuen Covid-19-Verordnung erregt seit letzter Nacht besondere Aufmerksamkeit: das Alkoholverbot nach der Sperrstunde im Umfeld von Bars. Weil kein Ende definiert ist, sind manche in Sorge: Sind wir auf dem Weg in eine Prohibition? Konkret heißt es in der Novelle, die am Sonntag in Kraft tritt: „Nach der Sperrstunde dürfen im Umkreis von 50 Metern um Betriebsstätten der Gastgewerbe (sic!) keine alkoholischen Getränke konsumiert werden.“ Die Sperrstunde liegt in den meisten Lokalen bei 1.00 Uhr.
For the first activity, the students’ task is to translate this into English. Beforehand, translate the text using DeepL, and scramble the words, giving a copy of this scramble to the students.
1.00 am 50 meters a a after after alcohol alcoholic amendment are are around attention attracting ban bars be because been beverages closing come consumed Covid 19 curfew curfew defined end establishments establishments force has hospitality in in in into is is last may most new night no no of of on on on one passage prohibition radius regulation sic! since some special specifically states Sunday the the the the the the time to vicinity way we which will within worried
The translation becomes a kind of jigsaw.
The second activity, only appropriate for more advanced learners, takes a text in the L1. Use two different translation tools to create separate translations, and correct any obvious errors (if there are any). Distribute these, along with the original to the students. Their task is, first, to identify and highlight any differences between the two versions. After that, they discuss each difference, saying which version they prefer (and why) or whether they have no preference.
Google Translate: One passage in the new Covid-19 regulation has been attracting special attention since last night: the ban on alcohol after the curfew in the vicinity of bars. Because no end is defined, some are concerned: are we on the way to prohibition? Specifically, the amendment, which comes into force on Sunday, says: “After the curfew, alcoholic beverages may not be consumed within 50 meters of the hospitality industry (sic!).” The curfew is at 1.00 a.m. in most restaurants.
Deepl: One passage in the new Covid 19 regulation has been attracting special attention since last night: the ban on alcohol after curfew in the vicinity of bars. Because no end is defined, some are worried: Are we on the way to a prohibition? Specifically, the amendment, which will come into force on Sunday, states: “After curfew, no alcoholic beverages may be consumed within a radius of 50 meters around hospitality establishments (sic!). The closing time is 1.00 am in most establishments.
One further activity that I would like to suggest makes use of the way that Google Translate translates each word as it goes, but amends previously translated words in the light of what follows. This is only suitable when Google Translate is accurate! The cleft example below (The thing that bothers me most is how long it will take) neatly illustrates the process. The following is a game-like exploitation. Project (or screen-share) Google Translate, set up to English and the learners’ own language. Tell the students that you are going to do a translation together. Tell them that the first word will be ‘the’, and ask them to predict how Google will translate it. Then, type in the word and everyone can see how Google translates it. Tell the students the next word (‘thing’) and again ask for their suggestions before typing it in. Carry on in the same way.
The
Das
The thing
Die Sache
The thing that
Die Sache, die
The thing that bothers
Das, was stört
The thing that bothers me
Das, was mich stört
The thing that bothers me most
Das, was mich am mesiten stört
The thing that bothers me most is
Das, was mich am mesiten stört, ist
The thing that bothers me most is how
Was mich am meisten stört, ist wie
The thing that bothers me most is how long
Was mich am meisten stört, ist wie lange
The thing that bothers me most is how long it
Das, was mich am meisten stört, ist, wie lange es dauert
The thing that bothers me most is how long it will
Was mich am meisten stört, ist, wie lange es dauern wird
The thing that bothers me most is how long it will take.
Was mich am meisten stört, ist, wie lange es dauern wird.
References
Aiken, M. (2019). An Updated Evaluation of Google Translate Accuracy. Studies in Linguistics and Literature, 3 (3) http://dx.doi.org/10.22158/sll.v3n3p253
Bowker, L. (2020) Machine translation literacy instruction for international business students and business English instructors. Journal of Business & Finance Librarianship 25 (1):1-19 https://www.researchgate.net/publication/343410145_Machine_translation_literacy_instruction_for_international_business_students_and_business_English_instructors
Kerr, P. (2014) Translation and Own-Language Activities. Cambridge: Cambridge University Press
Minh Trang, N. (2019) Using Google Translate as a Pronunciation Training Tool. LangLit, 5 (4), May 2019 https://www.researchgate.net/publication/333808794_USING_GOOGLE_TRANSLATE_AS_A_PRONUNCIATION_TRAINING_TOOL
O’Neill, E. M. (2019) Training students to use online translators and dictionaries: The impact on second language writing scores. International Journal of Research Studies in Language Learning, 8(2), 47-65
Tsai, S. (2019) Using google translate in EFL drafts: a preliminary investigation. Computer Assisted Language Learning, 32 (5-6): pp. 510–526. https://doi.org/10.1080/09588221.2018.1527361
Adaptive Learning in ELT 