At this year’s CEGLOC virtual conference, I watched a couple of presentations about the role of Machine Translation (MT) in language teaching and learning.

They got me thinking again about a subject I’ve written about a few times before and also edited an article on.

Here are a few key assumptions about the intersection of MT and language teaching/learning:

#1 The accuracy/naturalness of MT is continuing to improve. The output produced by MT is approaching the point where it is virtually indistinguishable from the output produced by human translators.

#2 It is unacceptable for a student to rely solely on MT when submitting work for an assessed course of language learning. An example of this would be if a Japanese student wrote a report entirely in Japanese, pasted the report into an MT tool, copied the resulting English, and handed in the work in as their own (without even looking at the resulting English text)

#3 Notwithstanding #2, MT could have powerful pedagogical applications if used in the right way. 

#4 It is difficult, if not impossible, to completely prevent the use of MT without reverting to hand-written essays in exam conditions. Unlike plagiarism, MT cannot be easily detected by software. Although there are still some tell-tale idiosyncrasies of translations produced by MT (such as inappropriate grammatical subjects when translating Japanese to English, for example) such traits/mistakes are becoming less obvious as MT continues to improve

#5 The kind of behavior exemplified in #2 is clearly not a “CALL” technique. Computer Assisted Language Learning entails the use of computers to assist the learning of a language. Using MT to generate a piece of assessed work and handing it in sight-unseen is basically indistinguishable from plagiarism. But instead of passing off the work produced by another human intelligence as their own, the perpetrator is passing off the work generated by artificial intelligence as their own.

The above assumptions (if correct) raise some interesting questions, and force us to re-evaluate the reasons or motivations for learning a language.

The motivation for learning a language is often categorized into three main strands: integrative, intrinsic, and instrumental.

Integrative motivation compels language learners who wish to live in and integrate with a target language community. This kind of motivation might drive an American who wants to emigrate to and settle down in Japan, for example. 

Would such an individual be able to rely solely on MT tools and devices to achieve this goal? Could they whisper sweet nothings into their iPhone, and then place the iPhone on their pillow and allow it to translate and convey those sentiments to their significant other? Perhaps not.

Intrinsic motivation comes from inside the individual and often arises from a deep interest in the target language itself. Intrinsically motivated students are interested not only in the syntactic structure of the target language, but also in how speaking the language will change the way they perceive and interact with the world around them.

Could an intrinsically motivated individual leverage the power of MT to further increase their knowledge of the target language? I think so. Would they be happy to completely delegate to MT the task of translating their thoughts from L1 to L2? Would they want to miss out on the philosophical or cultural insights that learning another language can bring about? I think not.

Instrumentally motivated individuals simply treat the target language as a means to an end. They want to get a promotion or avoid being demoted. They want (or have) to do business with speakers of the target language. They want to quickly translate an email or subtitles for a video (e.g., rev.com). They want to pass an exam or entrance test for a particular business organization or academic institution. 

Could such an individual rely extensively on MT to achieve their aims? I think so. Would it be fair to allow them to do so, especially with regards to assumptions #2 and #5 above? Perhaps not. 

That question would need to be decided by the organizations and institutions involved, who are best placed to judge the skills and competencies they require from candidates.

Given all of the above, I tend to believe that the use of MT tools and devices will continue to increase, especially in situations where instrumental motivation is paramount, or time and money costs are significant. 

But in my role as a language teacher who has to assess the written and spoken output of language learners, there are difficult questions to answer with regard to the role that MT can or should play in the language learning process.

MT surely has many powerful pedagogical applications, but the temptation for time-pressed and sleep-starved students to rely solely on MT to produce the required output is high.

And then we’re into the familiar territory of plagiarism – passing off another’s work as your own. Something most academic institutions seriously frown upon.

So, those are my current thoughts on MT.

Would love to hear others.