Language Teacher Education Vol. 6 No. 2, August 10, 2019

【Invited Paper】

The Implications of Machine Translation
for English Education in Japan
Tom Gally

Abstract
The performance of machine translation (MT) provided online for free by
Google and others improved significantly in 2016. MT is now increasingly
used in business, research, and government as well as in private life. Althoughstill imperfect, MT that is applicable for practical purposes challenges theprimary reason given for universal English education in Japan: that acquisitionof fluency in the language is essential for international communication.
Language educators considering how to respond to the challenge from MT facesignificant uncertainties, including what effect MT use might have on learners’
language acquisition and motivation, and whether and how MT should beincorporated in classroom curricula. After examining two simple but
illustrative examples of MT use in the real world—a sign in a convenience
store and an MT-mediated conversation overheard on a train—this paper
discusses what is known and unknown about MT and language education as of2019. It closes with a proposal to evaluate MT not by the sentence-by-sentence
accuracy of its translations but rather by means of “can-do” rubrics similar to
those of the Common European Framework of Reference for Languages(CEFR).
Keywords
machine translation, language in use, language acquisition, learner motivation, CEFR
1. Introduction
Since the dawn of civilization, a fundamental assumption of human interaction has
been that if two people who speak different languages want to communicate orcooperate with each other, then they must use a common language, one of them must
learn the language of the other, or they must rely on a human interpreter. This
assumption—that all but the most rudimentary interaction across linguistic divides
requires that people know more than one language—has driven the goals and methodsof second-language education as well.
However, it is now possible for people without a common language tocommunicate directly by means of machine translation (MT), that is, computer softwarethat takes textual or spoken input in one language and outputs a translation in another

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language. While the accuracy of the translations produced by MT is still a significant
issue, recent significant improvements in MT quality, enabled by advances in artificial
intelligence technology, have made MT usable, and used, in many real-life contexts.
This development has the potential to overturn the very foundation of second-language
education, forcing educators, students, policymakers, and others to start thinking about
the implications for language education and learning. Those implications, and the many
unknowns that affect them, are the focus of this essay.
Before describing the capabilities and limitations of MT in general, I will firstpresent two simple but illustrative examples of MT in actual use.
2. Two Cases of Real-World Use
2.1 A Convenience Store Sign
Figure 1 shows a sign that was displayed in an eating area on the second floor of aconvenience store near my home in Yokohama, Japan, in 2019. As is increasingly
common with multilingual signs in Japan, the English seems to have been prepared withMT.

Figure 1. A sign in a convenience store in Yokohama.
These three lines of English typify the lower range of quality for today’s MT. The
first line, “Works of Eat,” is supposed to be a translation of the two Japanese lines aboveit, which actually mean “Guide to using the eating area.” The second, “Available time,”
corresponds to a phrase in Japanese that means “The times when you can use [this area].”
Below that line appears the time range “am6:00 pm8:00”; while not punctuated in thestandard way for English, it should be understandable to both Japanese and English
speakers. The third line in English is “Drinking , smoking , can not be .”; thiscorresponds to the two Japanese lines above it, which mean “You may not drink alcoholor smoke.” At the bottom of the sign are two prohibition symbols, one the standard

Language Teacher Education Vol. 6 No. 2, August 10, 2019no-smoking sign, with a red slash through a lit cigarette, and the other a no-drinking
sign, with a similar slash through a mug of beer.
The three lines of English seem to have been composed using MT because, first ofall, the third line follows the verb-final grammatical order of the Japanese sentence; aJapanese speaker with even an elementary ability in English would be unlikely to createa sentence like this. (The MT user probably input the two lines separately; if they had
been input together, the MT output would have been more natural.) Second, the first line,“Works of Eat,” makes no sense; it seems to be the result of the translation engine’s
inability to handle the first line, a Japanese-English neologism based on the words “eat”
and “in,” meaning the area within a store for eating the store’s ready-to-eat food.
This sign typifies the degree to which even low-quality MT can be used forpractical communication. If a foreign resident or tourist able to read English but not
Japanese happened into the store and saw the sign, he or she would probably understandthe meaning of the phrase “Available time.” Although this English might seem opaquein isolation, there is sufficient context for the foreigner to guess the intended meaning: atime range appears immediately below it, the sign is posted in an unstaffed area that has
tables and chairs, and a customer who happens into the convenience store might
naturally be wondering whether it is acceptable to sit at those tables and eat. The thirdline, about drinking and smoking, is unnatural as English, but the symbols immediately
below it provide more than enough context to make it understandable as well. Only the
first line, “Works of Eat,” is likely to be incomprehensible even in context to a person
who cannot read Japanese. Nevertheless, the sign’s overall intended message—that
customers can sit and eat there from six in the morning until eight at night as long as
they do not smoke or drink alcohol—should be clear.
This ratio—about one-third of MT output being more or less natural, one-third
unnatural but understandable in context, and one-third incomprehensible—is typical for
MT today. If the translations appear in isolation, without textual or situational context,
they are often useless; with sufficient surrounding clues, however, they can, like thissign, serve their intended purpose in real-life situations. Furthermore, if the person who
prepared this sign had had moderate knowledge of English or of how to use MT
effectively, he could have fixed some of the errors and made the sign even more
understandable.
I have been shopping at this convenience store for nearly 20 years, and I know theowner slightly. The store is in a residential neighborhood that has few foreign residents
or tourists. The owner, who probably prepared the sign himself, appears to be in his
sixties. I have not asked him about his knowledge of English, but it would not be
surprising if he has not used or studied the language since high school. He has always
looked busy and tired when I have seen him in the store, which is open 24 hours a day,and it is unlikely that he would have had any time in recent years to review English, so
he has probably forgotten nearly everything he learned in school. It is even less

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conceivable that he would have had any opportunity to learn techniques for improving
the output of MT; in 2019, the development and promulgation of those techniques, suchas making sentence subjects explicit and back-translating the output into the source
language to check its meaning, are still in their infancy. His inexperience with both
English and MT would explain why two of the three lines in English are so unnatural:
he just accepted whatever output he got from a free online translation service and copied
it onto the sign. But despite all these problems, the sign serves its intended purpose.
2.2 A Tourist on a Train
A couple of months after I noticed that convenience store sign, I was sitting on a
crowded commuter train from Tokyo to Yokohama one Saturday evening when I
realized that two women standing near me were having a conversation in both Japaneseand English. We were so close that I could not help hearing every word that they said,
and I learned a little bit about them. Speaker A was a tourist visiting Japan, apparently,
judging by her accent, from another Asian country; she mentioned that, before coming
to Tokyo, she had stayed at a hotel in a popular resort in central Japan. She had asuitcase beside her, so she was probably in transit between hotels. Speaker B was a
Japanese college student. I did not hear the beginning of the conversation, but I guessthat it had begun a few minutes before, perhaps when Speaker A struck up aconversation with Speaker B to find out if she was on the right train.
Speaker A spoke only English. Speaker B spoke mostly Japanese, thoughsometimes, with effort, she tried to express herself in English as well. They were able tohave this bilingual conversation because they were using an app on Speaker A’s
smartphone that could translate spoken English into Japanese and vice versa.
Their success using the app was mixed. Some of what they said seemed to becorrectly translated and understood; some of their utterances were apparently
misinterpreted by the app’s voice recognition function and had to be repeated orrephrased; and some of their utterances seem to have been recognized but thetranslations could not be understood. (No spoken translation was audible. Instead, thetwo women confirmed the accuracy of the voice recognition and read the translations
from the other’s language by looking at the smartphone screen.)
The following, from memory, is a re-creation of part of their conversation. They
had just finished confirming that Speaker A would need to get off the train after two
more stops.
Speaker A said in English, “I plan to go to Place M in Yokohama tomorrow.”
Speaker B read the translation on the smartphone screen and replied in Japanese, “I
pass through Place M almost every day.” She looked closely at the screen as she said
this to confirm that it had recognized her words correctly.
Speaker A seemed to understand the English translation, and she said into the phone

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“How come?” After a pause, she said “How come?” again. And again. The phoneapparently did not recognize those words, so she rephrased herself: “Why do you gothere?”
Speaker B answered in Japanese, “My college is near there.”Speaker A replied, “Are you a college student?”
This time, Speaker B seemed to understand Speaker A’s words directly. Withoutlooking at the translation on the phone’s screen, she said in very hesitant English, “Yes,I am, uh, year two, uh, college.”
The rest of the conversation, which lasted until Speaker A got off the train a few
minutes later, was conducted in English. While Speaker A was fluent, Speaker B was
clearly not used to speaking English and had to make an effort to express what she
wanted to say. I could see Speaker B’s face as she spoke: she looked both excited to beusing her limited English to communicate with the tourist and embarrassed at not being
able to speak better. During the part of the conversation unmediated by the translation
app, Speaker A adapted her spoken English and used gestures to help Speaker B
understand.
While by no means smooth, the conversation—at least the part I heard—seemed
successful overall. In addition to the practical discussion about where Speaker A should
get off the train, Speaker A was also able to tell Speaker B about the hotel where she
had stayed at the resort. While some information exchange would have been possible
even without the translation app, the level of the conversation was somewhat richer than
it would have been if Speaker B had had to rely entirely on her limited English speaking
ability. (I assume that Speaker A knew no Japanese.)
2.3 Some Implications
The example of the convenience-store sign shows why many people have started touse MT. If my assumptions about the store owner are correct, then, when he was inschool five decades ago, he was required to study English as a foreign language. There
were several reasons for that requirement. First, it was, and still is, believed thatlearning a foreign language—any foreign language—benefits learners by exposing themto other cultures, to other people, and to a way of communication that is different fromtheir first language. Second, English ability acquired through study is regarded as anindicator of one’s intelligence and diligence, and ability in the language is tested on
entrance examinations for universities and colleges. And the third is that, in the
globalizing world of a half century ago and even more so in that of today, it is believedthat many people will need the ability to use English, the international lingua franca, atsome point in their lives.
For a convenience store owner in his sixties in that part of Yokohama, preparing asign in Japanese and English for his store is probably one of the few times he ever has aLanguage Teacher Education Vol. 6 No. 2, August 10, 2019need for English. Having long forgotten nearly everything of the language he learned
when he was young, he faced three options: devote many hours of his very limited timeto trying to relearn enough of the language so that he could compose the English for thesign himself; pay a translator (if he knew how to find one) to prepare the sign for him;or spend a few minutes on his smartphone or computer to get translations using free
online translation tools. It is no surprise that he chose the last option. It would be evenless surprising, if he decided to prepare similar signs in Chinese, Korean, Thai, or theother Asian languages that foreign customers in that part of Yokohama are more likely
to know, that he would use MT for those languages as well rather than trying to learn
their writing systems, vocabulary, and grammar from scratch. MT thus offered a quick,easy, and very cheap solution to a practical problem.
The train conversation shows how MT can also encourage and enable interpersonalcommunication. While Google and others offer free smartphone apps for interpretingspoken conversation, and specialized devices for the same purpose are widely
advertised and sold in Japan, when I have observed them in actual use, both in a handfulof real-life situations such as on the train and in some formal experiments (Kōchiyama,
Liang, Wrobel, Kawakita, & Gally, 2018), conversations using them have been only
barely satisfactory. The frequent misrecognition of the participants’ speech and theoccasional mistranslation of correctly recognized speech make the interpreted
conversations a frustrating experience even for users who are able to adapt their speech
to make what they say easier for the software to recognize and translate. Until effective
techniques for using the software are better understood and more people are trained andexperienced in those techniques, the apps’ and devices’ main application will be simple
tasks, such as asking and giving directions. Holding a conversation even through aprofessional human interpreter can be exhausting; few people would have the patience
to conduct an extended conversation or discussion if it had to be interpreted by today’s
software.
But despite their limitations, the software and devices for conversational
interpretation are seductive in a way that the more reliable written translation is not. By
enabling face-to-face conversation between people without a common language,
communication that is embedded in a rich context of gestures, facial expressions, speechintonation, and even physical contact, they enable the establishment of person-to-person
connections that might not otherwise be formed. Those connections might, in somecases, motivate people to try to acquire each other’s language so that they cancommunicate more directly. The Japanese college student I observed on the train, forexample, was clearly enjoying the opportunity to talk with the foreign tourist,
something she might have avoided had she been forced to try to speak only in English
from the beginning. While that particular interaction ended when the tourist got off the
train, one can easily imagine someone who has made a friend using an interpretation
app feeling inspired to try to learn the friend’s language.

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One of the main reasons that has long been given in Japan for teaching all children
English—that it might be useful at some point in the future—is made less convincing by
the ready availability of reasonably accurate MT (Gally, 2018). When that convenience
store owner needed to write in English, the software could do the job adequately for his
purpose; a little training in how to use MT would have made the results muchbetter—even if he still had no actual English ability. When a Japanese person is asked
directions by a foreign visitor—a situation often presented to children as a reason tolearn English—the necessary translations can now often be obtained through an app.
And those translations can be made not only to and from English—which is the only
foreign language that nearly all Japanese schoolchildren study—but to and from manyother languages as well, including the Asian languages spoken by the majority offoreign visitors to Japan.
Before discussing the implications of MT’s improved capabilities forEnglish-language educators in Japan, I would first like to zoom out and give a brief
overview of MT as it exists today.
3. The Quality of Today’s MT
Although computer scientists have been developing MT systems since the 1950s,
MT became usable for practical purposes, especially between languages as different as
Japanese and English, only in late 2016, when Google shifted its free online translation
service to a new, neural-network-based system (Lewis-Kraus, 2016; Wu et al., 2016). At
that point, the naturalness and grammaticality of the output improved significantly.
Unlike human language users, however, translation software still does not seem totake knowledge about the real world—meaning, in other words—into account. Words
with multiple senses—such as “scale” in English or suji (筋) in Japanese—are often
mistranslated, and syntactic ambiguities—such as “The teacher pointed at the studentwith a pen”—are often misinterpreted. The free software currently available online does
not advise users when such ambiguities are present, so people who do not know boththe source and target languages cannot easily determine whether or not MT output isaccurate.
The neural networks that provide the translations have reportedly been trainedusing bilingual corpora of human-translated texts. Perhaps because such corpora,especially those available for free online, tend toward relatively formal types ofwriting—news articles, patents, government and corporate websites, etc.—MT performs
much better with such texts than, for example, with informal dialogue from novels or
television dramas. The sentences in formal texts also tend to be less context-dependent,
making MT more applicable for academic and technical writing, which tends to be moresemantically explicit at the sentence level, than for colloquial language, which often
depends on an understanding of the situation in order to be comprehended andLanguage Teacher Education Vol. 6 No. 2, August 10, 2019translated.
The above example of the convenience store sign illustrates several aspects of how
MT succeeds or fails in practice. It functions best when the translation is used within arich situational and linguistic context. The context in that case was the sign’s location ina convenience store where ready-to-eat food is sold; the nearby tables and chairs,suggestive of an informal café; the numbers and letters on the sign indicating a time
range; and the symbols prohibiting smoking and drinking. Such a context enables thereader to figure out the sign’s intended message; in isolation, perhaps two-thirds of thesign’s English would have been incomprehensible.
Similarly, translated conversations like the one I overheard on the train are mademore comprehensible by the rich context that typically occurs when people talk face toface: gestures, body language, facial expressions, vocal intonation, and the physicalsurroundings all help the participants interpret each other’s intended meaning evenwhen the translation app’s performance is inadequate.
Linguistic context—that is, the other words in the text or utterance—is also
important. MT from Japanese to English becomes significantly more comprehensible
and correct when the Japanese is pre-edited to make the subjects and objects of eachverb explicit. Subjects and objects of sentences are often omitted in Japanese when they
will be understood from the context; if no subject is present in the Japanese input, MT
tends to default to “I,” “you,” or “it” as the subject of the output sentence—regardless of
the intended meaning. While sentences with all subjects and objects explicit can makefor unnatural Japanese, the resulting English translations will usually be more correct.
Of course, texts can be pre-edited to be more machine-translatable only by someone
who is both fluent in the source language and adept at using it. A person unable to readJapanese, for example, would not be able to make such adjustments before using MT totranslate a Japanese text into English; even a native speaker of Japanese, if he or she
lacked sufficient explicit awareness of the language’s grammar, might have difficulty
making the adjustments as well.
An ironic weakness of current online MT systems is that they are very easy to use.
They are available for free on both computers and smartphones, and they accept
whatever is input by the user—even if it is poorly formed or ambiguous. The Google
search engine will sometimes offer a translation of a search term even when the user did
not explicitly ask for one. Because anyone using a computer or smartphone can get
translations into dozens of languages with little effort, MT is now being used widely bypeople who do not understand how to use it effectively or how it can fail. In fact, eventhose of us who have been experimenting with the latest MT software intensively for thelast few years do not yet really know the optimal methods for using it. A few techniques,
such as making sentence subjects explicit, seem clear, but many other issues, such as
how best to confirm the correctness of translations from or into languages one doesn’t
understand, are still unresolved.

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Another issue is that the main providers of current MT systems are privatecompanies that are very careful about revealing their business strategies and protecting
their intellectual property. Google, for example, made its neural-network-based MT
system available with almost no fanfare in late 2016. While that system performedmuch better than the previous systems, Google has provided little information about
how the system works, what sorts of texts are used for analysis by the machine learning,
how frequently and to what extent the neural network that produces the translations is
updated, and what plans the company might have for improving or changing thetranslation process or interface in the future.
The business models for MT are also unclear. Presumably Google, Microsoft, andothers hope to recapture their considerable investments either through advertising,including the increased exposure made possible by translating advertisements
automatically, or by charging users for their service. There is no guarantee that theservices will be as easy and convenient to use, or even available for free at all, five or
ten years in the future. This makes it difficult for users, including educators, to knowwhether and how to incorporate MT into long-term plans.
4. The Unknowns for Educators
Just as MT technology’s present and future are full of uncertainties, theimplications for language education are obscured by many unknowns as well.
4.1 Real-world Use
One reason I opened this essay with informal observations of the use of MT in
Japan is that I have been unable to find any systematic survey data of how it is actuallyused. In 2018, Satoshi Kurokawa conducted a small online survey of researchers inJapan about their use of MT; the results indicated that about two-thirds of the 141
respondents said they used it in some form, though their ways of using it varied widely(Kurokawa, 2018). My informal questioning of businesspeople, shopkeepers, taxi
drivers, and others suggests that many people use the tools when they feel the need, but
no clear picture has yet emerged about how widespread that use actually is or about towhat extent its use might affect perceptions of the need for actual ability in English orother languages. Since universal English education in Japan is predicated largely on the
belief that many adults must use English for their jobs, much more needs to be learned
about how the ready availability of MT might be affecting the use of English, andperceptions about the need for English, in real life.
4.2 Effect on Language Acquisition
When I saw the improved output of Google Translate in late 2016, one of my firstreactions was to wonder about the effect on foreign-language learning. If university

Language Teacher Education Vol. 6 No. 2, August 10, 2019students studying how to write academic papers in English, for example, were to cometo rely on MT when writing in English, would their autonomous ability to use English
decline?
Some relevant analogies seemed to be to electronic calculators, Japanese word
processors, and car navigation systems. When portable calculators became widely
available in the 1970s, many people noticed that they became less able to do arithmetic
with a pencil and paper. Similarly, using Japanese word processors, which convert
phonetic input into kanji characters, seemed to worsen people’s ability to remember
how to write individual kanji by hand as they had learned in school. And reliance on car
navigation systems has been said to weaken drivers’ ability to find their way around on
their own.
Research conducted by Mikie Nishiyama and others, however, suggests that theseanalogies might not always apply to MT (Nishiyama, 2017, 2018). In one experimentthat she conducted, for example, she observed Japanese college students writing
compositions in English using Google Translate. Some students composed their originaldrafts entirely in Japanese and then just ran the Japanese through Google Translate toproduce the requested English text; it seems likely that long-term use of MT in this way
would not benefit, and might even harm, those students’ acquisition of English. In
contrast, other students engaged with the English output more interactively, reading it tocheck its meaning, looking up the meanings of words they didn’t know, and adjusting
the English themselves before finishing their composition; such strategies, by making
the students engage actively with the MT-produced English, might actually aid language
acquisition.
But this is only speculation. Not only has no long-term, large-scale research been
conducted yet to try to determine definitively how the use of MT might affect language
acquisition, there is very little subjective evidence either. Ten or twenty years from now,there will be many people who will be able to describe how using MT seemed to affecttheir foreign language acquisition when they were younger. At present, however, we canonly guess
4.3 Effect on Motivation
Also unknown is how the availability of MT might affect Japanese students’motivation to study English. To the extent that students are motivated by a perceived
need to use English, the growing everyday use of this convenient new technology seems
likely to weaken that motivation. But students have other reasons to study foreign
languages as well, including attraction to the people and cultures associated with thelanguage, interest in the grammar, vocabulary, and other linguistic aspects of thelanguage itself, and the visceral enjoyment of using a second language. Motivations
such as these seem less likely to be harmed by MT.
The strongest immediate motivation for Japanese schoolchildren to study English,

Language Teacher Education Vol. 6 No. 2, August 10, 2019of course, is admission tests to higher-level schools and universities. The popularprestige of high scores on exams such as TOEIC drives many adults as well to continue
or renew their study of the language. It is unlikely that test-takers will be allowed to use
MT during exams in the foreseeable future, so some learners will continue to be
motivated to acquire at least the English required for tests without depending on MT. In
the longer term, however, the ready availability of MT may call into question thewisdom of placing so much emphasis on test-taking ability, which may come to beperceived as an increasingly esoteric skill.
4.4 Applicability to Education
These unknowns—about real-world use and about the effects on language
acquisition and motivation—make it difficult to know whether and how language
teachers should respond to the improvements in MT. Should they incorporate MT in
their lessons? If so, how? Should they try to discourage or forbid its use? Or should they
just ignore it? Each response is probably suitable in certain educational contexts, but nogeneral insights have yet emerged about which strategy is best for specific types ofsituations and students.
In writing classes, for example, the issue of whether using MT when composing
assignments is “cheating” remains unresolved. So too is the problem of how to identify
and deal with plagiarism when MT is used. When a typical Japanese university student
cuts and pastes text from, say, the English version of Wikipedia for a class assignment,
the teacher can usually detect that copying easily, both from the suspiciously correct
English and by finding the original text through a web search. However, if the same
student copies text from the Japanese Wikipedia and runs that through Google Translate,the plagiarism can be much more difficult to detect, especially if the student adjusts the
input and output to remove obvious strangeness.
Students’ attitudes about the use of MT are also unknown. Some might embrace
the incorporation of MT in the language classroom, others might resist it, and others
might be indifferent. The current generation of teachers did not use MT themselves
when studying foreign languages, so we need to discuss it with our students and find out
what they think about its use in language study.
4.5 Language Learning for Personal Enrichment
While the need for English ability for practical uses ( itsuyō in apanese is theprimary ustification for uni ersal n lish education in apan, it has also een ustified
in terms of yōyō, or personal enrichment throu h road learnin ( amamura,
Gakutani, Karpinska, Tanojiri, & Gally, 2019). In that context, the supposed benefits oflearning English include knowledge and insights about other cultures and people; themental discipline of studying explicitly a foreign grammar and vocabulary; and greaterawareness of one’s mother tongue acquired in contrast with, and through translation toLanguage Teacher Education Vol. 6 No. 2, August 10, 2019and from, another language.
It is unclear whether such goals would be furthered or hindered by incorporating
MT in the classroom. On the one hand, the ability to translate online texts from otherlanguages into one’s first language enables one to read texts from around the world thatwould not otherwise be accessible; students using translation apps would also be able tohave conversations with, and thus learn about, people from many other countries. On
the other hand, MT obviates the need to focus on another language’s vocabulary andgrammar, which can, in conventional language learning, yield insights into thelanguage’s speakers and associated cultures that are not easily obtained throughtranslation. As with the possible effects of MT use on long-term language acquisition,
we may have to wait years, even decades, until a consensus emerges about whether or
not MT can play a constructive role in a liberal education.
5. What Is to Be Done?
In light of the many uncertainties about MT, it is tempting for language educators
to ignore it as long as they can and to continue teaching as they have in the past. Thatmay be a reasonable choice for some teachers, such as those primarily concerned withtest preparation or foreign-language literature. For most English teachers in Japan,
however, the conflict between the practical, communicative focus of their curricula andthe increasing use of MT for real-life communication means that ignoring the issue will
make those curricula increasingly untenable. I would therefore like to make a few
suggestions on how we might move ahead.
Instead of evaluating the potential impact of MT based on the quality of thetranslations themselves, educators should focus instead on how effectively MT can beused in real-life tasks. When comparing MT input with MT output, it is easy to findtranslations that are horribly, even laughably wrong. But the challenge of MT for
language education comes not from translation accuracy; rather, it comes from the factthat this amazingly convenient tool is being used, often effectively, by people who needto get things done in the real world. As shown by the examples of the convenience store
sign and the train conversation discussed earlier, in rich situational or interactive
contexts, MT can fail completely at the sentence level while still enabling the users toaccomplish what they want to do.
A promising framework for evaluating MT is one familiar to many language
educators: the Common European Framework of Reference for Languages (CEFR)(Council of Europe, n.d. [2001]). The CEFR “can-do” rubrics for evaluating whether
people can perform certain tasks in a second language could easily be adapted toevaluate the performance of such tasks using MT. A B2 descriptor for reading, forexample, might be “Using MT, I can understand the main content and key points ofarticles and reports about contemporary issues,” while an A1 descriptor for writing

Language Teacher Education Vol. 6 No. 2, August 10, 2019

might read “Using MT, I can produce short, simple sentences that convey my intended
meaning.”
My guess is that most people who have experience using MT would assessthemselves as scoring at the A2, B1, or B2 level in most categories regardless of thetarget language—levels that, for conventional language use, can usually be obtained
only after years of study. As the CEFR scales are increasingly being used in language
education in Japan, including for university admissions, confirmation or refutation ofthis guess through systematic studies would clarify the implications of MT for
curriculum development and education policy. Perhaps some ambitious educational
entrepreneurs could even create a “CEFR-MT” qualification test to formalize theassessment of people’s ability to use MT.
The unanswered questions about the effect of MT use on acquisition andmotivation can be tackled in three ways. First, when possible ethically and practically,language teachers should experiment with MT in the classroom and try to gauge theimpact on student learning and attitudes. That impact is likely to be complex and to varywith the curriculum and with the levels, motivations, and individual learning styles ofthe students, so teachers should share what they learn widely with colleagues until abroad understanding of the impacts of MT begins to emerge. Second, language
educators and other experienced language learners should try using MT themselves intheir own private study of other languages; their subjective experiences, if shared widely,
will yield insights into how younger students might or might not be able to use it in theirlearning processes. And third, researchers in the field of second-language acquisitionand learner motivation should conduct quantitative and qualitative studies of the effectsof MT use and develop new theoretical frameworks to understand now thispseudohuman technology might affect real human language use.
The arrival of usable MT has overturned some basic assumptions of language
education. No longer do communication and cooperation between people who do notshare a common language have to be mediated by a person who knows more than one
language. No longer can it be convincingly argued that acquiring a second language isnecessarily worth the immense time and effort required. The widespread availability of
MT will lead to growing dissatisfaction with current language education policies andmethods and to increased pressure for fundamental reforms. Language educators need tothink deeply, both individually and collectively, about how best to respond.

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References
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