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Digital Translation: Its Potential and Limitations for Informal Language Learning

HELEN SLATYER AND SARAH FORGET

Introduction

Translation and foreign language learning and teaching are historically and conceptually linked through their common goal of communication (Rogers 2000). The association is believed to have started with the teaching of the classics in Europe in the Middle Ages (Weihua 2000). In the late‐eighteenth century, the grammar translation (GT) method was developed in Prussian secondary schools with the aim of making the teaching of modern languages more respectable by emulating the traditional teaching of the classics (Cook 2010). GT was one of the earliest systematic language teaching methods that used translation in the modern language classroom (Richards and Rodgers 2014). However, by the middle half of the twentieth century it was discredited and is still considered negatively by teachers (Pym et al. 2013) due to the introduction of the first of the direct methods which promoted teaching the foreign language intralingually (using only the language that was being acquired for all communication in and outside the classroom1). At the same time bilingual2 foreign language teaching and learning was also rejected. Direct methods were superseded in the 1970s by meaning‐based methods such as the communicative method, which also focused on using only the foreign language for learning, in this case in naturalistic, communicative contexts with the aim of encouraging students to focus on conveying meaning (Cook 2010). Consequently, for the next 50 years the use of the L1 in the foreign language classroom (in particular for the learning of English as a foreign language) was largely outlawed.

According to Cook, the rationale for the exclusion of the native language (L1) is based on four erroneous assumptions:

  1. Languages are monolingual.
  2. Language learning should reproduce naturalistic acquisition similar to the native language in childhood.
  3. The aim of foreign language learning is to achieve native‐speaker‐like language use.
  4. Exclusion of the L1 is the “true” path to success.

These assumptions are not supported by any systematic second language acquisition research and have been argued from (principally) theoretical perspectives (Cook 2010, p. 8). To this list of reasons for abandoning the use of translation, Malmkjaer (1998) adds that translation, as a skill, is substantively different to the four core skills which define language competence (reading, writing, speaking, and listening) and is consequently seen as a waste of time. Because translation is text‐bound, it is also considered to be antithetical to the drive for communicative (oral) tasks (Duff 1996). Dagilienè adds that translation is “boring both to do and to correct” (Dagilienè 2012, p. 125), a widely stated reason for the fall from favor of the integration of translation tasks in the language classroom. Many in the TESOL (Teaching English to Speakers of Other Languages) field and English language schools still apply an “English‐only policy” (Kharchenko 2018) to prevent learners from using their L1.

Despite the dominance of intralingual methods, bilingual approaches to language learning have nevertheless endured in some sectors of foreign language learning and teaching and were even strongly recommended by some sectors of applied linguistics (Cook 2010). In the classroom, bilingual teaching is particularly appropriate where the students share a common language and the teacher knows that language. It is also predominant in modern language courses at university level (Malmkjaer 2004). Translation, as characteristic of some bilingual approaches, is also more widely used in the teaching of LOTEs (languages other than English) than it is in TESOL (Malmkjaer 1998) and is now frequently being referred to as an additional skill, “the fifth skill,” to the four core skills of reading, writing, speaking and listening, (Cook 2010; Pym et al. 2013; Dagilienè 2012).

Part of the toolkit for language learners has been the use of bilingual dictionaries for quick access to the full meaning of new words and phrases. The evolution of technologies for automating the processes of translation has meant that bilingual dictionaries have largely been replaced by online translation tools for professional and student translators. What translation tools have language learners adopted for their learning to replace, or add to, bilingual dictionaries and contrastive grammars and how are they using them?

In the following sections of this chapter, we provide a more detailed overview of how translation has been used to support language teaching and learning. We then describe the evolution of machine translation (MT) and identify which translation technologies are being used for language learning, with a focus on translation apps, and outline how language learners use these apps. We evaluate the effectiveness of these tools as a replacement of or supplement to traditional language teaching and learning resources and conclude with a discussion of whether the use of translation apps can be beneficial or detrimental to the acquisition of language by new and seasoned language learners.

Translation in the language classroom

In this section we provide a brief discussion of some of the main types of translation activities used in classroom settings as a useful starting point to better understand the potential for the use of translation tools. Pym et al. (2013) conclude from their comprehensive study of translation in language teaching that translation is not to be considered as a method in its own right (with the exception of GT). Translation, then, is mostly integrated into other pedagogical methods. Definitions of translation for language learning explicitly include oral translation and subtitling activities as well as written translation (Pym et al. 2013; Malmkjaer 1998; Cook 2010).

Much of the teaching of translation for future translation professionals commences with an equivalence‐based approach that examines translation choices at each linguistic level (lexical through to whole text and culture). However, translation used for the purpose of language acquisition often appears to aim for a close equivalence on a formal (syntactic) level which would be inappropriate for professional translation. Translation exercises for language learning could, therefore, be considered to be a specific type of translation. Cook (2010) defends the use of word‐for‐word or “literal” translation as a means of examining the mismatches in the two language systems, forcing a focus on what is different (lexically, syntactically, or pragmatically, for example). The use of contrastive approaches such as this reflects the comparative stylistic work in translation studies (Vinay and Darbelnet 1958) that aimed to help translators understand the linguistic choices (or “shifts”) that were involved during the translation process.

In addition to traditional translation exercises, more creative uses of translation have emerged:

  • communicative translation and dialogue interpreting by learners (increasingly conceptualized as forms of “mediation”),
  • identification of problems in machine‐translation output and their correction,
  • the use and production of subtitled and dubbed video material (Pym et al. 2013, pp. 6–7).

Translation has also been used specifically for vocabulary acquisition, in particular to provide translation equivalents for new words. Notable is Laufer and Girsai's (2008) experiment that compared learners' performance in the retention of new vocabulary. Of the three groups that learned the new words, those that learned through contrastive analysis and translation outperformed the other two groups (meaning‐focus and form‐focus without translation). Of the methods briefly discussed here, contrastive analysis and the translation of vocabulary are the two activities most easily accessible using translation apps.

Translation technology

Technology developed for commercial translation purposes, computer‐assisted translation, currently falls into three degrees of automation as shown in Table 29.1:

  • fully automated translation (automatic translation),
  • computer‐assisted human translation (computer‐assisted translation), and
  • fully human translation (manual translation).

Machine‐assisted translation is a broad term used to describe a range of tools and approaches. It can be represented as a continuum of automation ranging from fully human translation3 without intervention of technology of any sort, to fully automated translation without human intervention, such as that performed by Google Translate or Microsoft Translator. An intermediate stage could be described as computer‐assisted translation, where human translation is assisted by computer‐based tools such as word processors or online dictionaries. Translation environment tools (TenTs), which include translation memories and terminology databases, are also now an integral part of the translator's toolkit.

Table 29.1 The evolution of translation technology.

Source: Adapted from Hutchins and Somers (1992).

More automation Less automation
No image found.
Automatic translation Computer‐assisted translation Manual translation
Machine translation
  • Google Translate
  • Microsoft Translator
  • SYSTRAN, etc.
The translator's toolkit Word processor
Spell‐checker
Dictionaries
Thesauri
Encyclopedias
Bilingual dictionaries
Online references
Translation Environment Tools
Pen and paper translation
Paper dictionaries

In this section, we focus on automatic translation technology which is used by language learners all over the globe through the use of translation apps. We first outline the different types of MT that exist and identify how they have evolved. We then review the existing studies surveying the use of apps (not only translation apps) by language learners and finally provide an overview of current translation apps.

The evolution of machine translation

When computers were developed, one of the original goals was to translate automatically from one language into another. Indeed, the Georgetown–IBM experiment was an influential demonstration of machine translation, performed on January 7, 1954. Developed jointly by Georgetown University and IBM, the experiment involved the fully automated translation of more than 60 Russian sentences into English (Hutchins 2004). The science of machine translation is therefore fairly old, but the results obtained nowadays still remain far from optimal. Over the past 60 years, MT systems have continued to evolve with technological advances and our own understanding of those systems.

Machine translation started as a rules‐based engine. Rules‐based machine translation (RBMT) involves the “explicit use and manual creation of linguistically informed rules and representations” (Habash et al. 2011, p. 133). RBMT works by analyzing the source language and breaking up the translator's activities into small chunks. Developers have to add information about the syntax of the language, the grammar of each word, as well as information about word collocations to identify the most appropriate translation according to context. Therefore, the work of the machine is divided in subtasks that are run sequentially. The creation of numerous rules for each language pair, each with exceptions, quickly proved inefficient, costly, and unsustainable in the long term. It was also assumed that the RBMT must be able to understand the underlying meaning of a text using the rules created. However, rules‐based MT works on a sentence‐level, meaning that the context of the overall paragraph or text cannot be taken into consideration. This realization led developers toward statistical MT.

Concurrently, in the 1990s, IBM researchers were working on vocal recognition and developing a statistical model of automatic rules extraction. Statistical machine translation (SMT) was born. SMT is essentially corpus based, i.e. the machine learns from a corpus of translation examples called “parallel or bilingual corpora” (Habash et al. 2011, p. 133). SMT implies that the MT tool analyzes a large quantity of bilingual texts to decide on percentages of accuracy between a source and a target. Since 2000, SMT engines have been trained using data that mostly originates from the European Parliament, the Canadian Parliament (for French and English), and the United Nations (Yvon 2018). The machine reproduces the type of translation observed during the training, which means that it is easier for a machine to translate administrative texts than more informal or casual texts due to the type of data being fed to the engine. The machine has a plethora of data to base itself on to produce a resaonably accurate translation. Moreover, the texts fed to the machine during training have been translated by professional translators and are therefore of good quality. However, the statistical model of machine translation lacks the linguistic knowledge of a rules‐based model. Developers therefore turned their attention to hybrid approaches to counter these drawbacks.

A new approach that has gained currency is neural machine translation (NMT) based on the biological structure of the human brain that uses artificial neural networks (Doherty 2017). Neural translation uses the context of the sentence, grammar, and sentence structure to create a translation that is more natural. This type of translation is closer to how a human would solve complex translation problems (Yvon 2018). “This is achieved by using a computer to simulate how interconnected brain cells process complex information” (Doherty 2017). The neural system also translates whole sentences instead of focusing on segments. The provision of more context helps the engine to choose the best possible translation for a sentence, which is then reorganized to produce a more natural translation that is closer to human discourse (Turovsky 2016).

Microsoft Translator and Google Translate have recently moved to NMT. In 2016, Google Translate launched its NMT engine for translation to and from English and eight other languages: French, German, Spanish, Portuguese, Chinese, Japanese, Korean, and Turkish.

Translation technology for language learning

Having gained an understanding of how machine translation engines work, let us now focus on translation apps in an attempt to understand why they are used so widely. They have a bad reputation among linguists, who see them either as a technology that is going to deprive them of their job (translators) or as a technology that generates incorrect, unnatural language with grammar mistakes (linguists, language learners, teachers, native speakers) (Garcia and Pena 2011; Groves and Mundt 2014; Jiménez‐Crespo 2017; Enkin and Mejías‐Bikandi 2016).

In an era where we are used to having everything readily accessible through technology, translation apps may appeal to language learners as a quick and easy way of finding solutions to their language problems. But should learners be relying on these apps for their learning – firstly, to find the meaning of a word or a sentence, and secondly to address other issues, such as finding synonyms, checking the pronunciation or the grammatical class of a term, among other things that we discuss later in the section? This raises the question of the possibilities offered by translation apps and what their evolution will mean for language learners.

The majority of research studies on the use of digital tools for language learning examines how they are used specifically in formal learning (i.e. inside the classroom) and informal learning (i.e. outside the classroom) (Lai and Zheng 2017). Concurrently, other research studies have evaluated the use of mobile devices in language learning in general (Bourne 2014; Lai and Zheng 2017; Elega and Özad 2017; Jin and Deifell 2013). The few studies that have examined the use and value of translation apps for language learning, mostly focus on Google Translate (Bahri and Mahadi 2016; Groves and Mundt 2014). There are, as yet, very few studies on the specific usage of translation apps for language learning using a broader range of translation apps.

Amongst the available research on the use of translation apps for language learning, a study by Elega and Özad (2017) investigated how Nigerian students learnt the Turkish language to interact with others in the community using Google Translate, iVoice, and iTranslate, where 51.7% of the students admitted using Google Translate to quickly grasp the meaning of Turkish words. Another study by Jin and Deifell (2013) focused on the use of online bilingual dictionaries to understand and create written texts in a foreign language. A study by Garcia and Pena (2011) researched whether MT could be used to develop writing skills for beginners and intermediate language learners. Finally, Bahri and Mahadi (2016) examined how Google Translate could be used as a supplementary tool to formal classroom learning of Bahasa Malaysia.

According to Elega and Özad (2017), software such as Google Translate has helped language learners acquire basic literacy skills in another language. Multiple studies also reveal that language learners almost always use Google Translate to support their learning (Bradley et al. 2017) using the translation of items of vocabulary or grammatical structures to enhance their comprehension.

Another factor playing a role in the wide usage of translation apps is the advances in mobile technology and the availability of free, downloadable apps. Anyone with a mobile phone can now make use of translation tools, which, as a result, have become ubiquitous in our daily lives. These apps increase engagement of learners and allow them to quickly solve language problems. We know that international students spend more time outside the classroom than they do inside (Elega and Özad 2017) and need easy and fast access to learning resources. The availability of translation apps anywhere, anytime, means that they use them more and more as a way to produce a receptive translation, i.e. a translation that will help them understand something in a foreign language (Lai and Zheng 2017). In a study carried out in 2017, newly arrived Arabic‐speaking migrants also consider translation apps as a language‐learning tool and report using them on a frequent basis to learn Swedish (Bradley et al. 2017).

It is undeniable that translation apps are used by language learners as we have seen from the studies reviewed in this section. However, little is known about what learners get from translation apps. Despite the multitude of reviews that can be found online, users only provide insights into the quality of the output from the machine. We do not have much information about how language learners potentially benefit from the tools on their journey to learning or improving a language, or if what they learn from these apps is accurate or not.

The translation app landscape

Despite the availability of a plethora of translation apps, Google Translate seems to remain one of the most widely used translation apps in the world. The fact that it evolves with the latest advances in MT, now using NMT, and that it relies on a large amount of corpus data for its statistical MT means that the results produced are often acceptable if not completely correct and accurate. This seems to draw users repeatedly to continue using Google Translate for their personal benefit. Below, we introduce and review four translation and language‐learning apps that we have used to test the accuracy of their translations of some simple expressions that could be used by learners. The translation apps have been selected on the basis of the three different engines that they use. Google Translate and iTranslate are available as mobile phone apps, whereas DeepL is only available as an online application accessible on a computer. It is also interesting to note that Google Translate and iTranslate provide a voice‐recording feature.

Google Translate

Google Translate was launched in 2006 with two languages. In 2016, the application supported 103 languages (this is still current in 2019). According to Google, more than 500 million people use Google Translate on a daily basis, most commonly in the combination of English and Spanish, Arabic, Russian, Portuguese, and Indonesian (Turovsky 2016).

As Groves and Mundt (2014) suggest, Google Translate includes a degree of “interactivity with its end‐users” as it uses its Translate Community platform to improve translation output. “[In 2016,] 3500 students translated and verified more than 4 million words and phrases in [Hebrew]” (Reshef 2017). The Google Translate Community, which comprises over 3 million members is an open, online group created by Google to improve its MT engine. They review, adjust, and correct the translations suggested by the community before feeding them to Google Translate, which uses these data to train its engine (Turovsky 2016).

The Google API (Application Programming Interface), developed by Google, lets any developer integrate Google Translate within their own software or application. This allows third‐party applications to benefit from the MT services provided by Google. Many translation apps have their own interface but work off the Google translation engine by integrating with the Google Translate API. Readlang, introduced later in this section, is one such app. It is also interesting to mention that many TenTs, the dedicated professional tools for translators, also integrate with Google Translate.

iTranslate

iTranslate is a translation, dictionary, and verb conjugation app powered by the SYSTRAN machine translation engine (ONE 2016). When searching for the best translation apps online, iTranslate often comes up in the Top‐5 list.

DeepL

DeepL is a new machine translation engine launched in August 2017, which uses deep learning and NMT to translate automatically from one language into another. The translations created by DeepL are more natural than those of other engines as you can see from the results in Table 29.2. To prove itself in a competitive environment, in August 2017, DeepL created a list of 100 sentences and translated them with DeepL Translator, Google Translate, and Microsoft Translator. The translations were assessed in a blind study by professional translators, who didn't know which system produced which translations. Translators chose the translations from DeepL as being the best 30% of the time (DeepL 2017). In March 2018, DeepL also released its API which allows other apps and software, such as Trados and Memsource, two professional translation environment tools, to integrate with the DeepL engine.

Readlang

Readlang is not a translation app like Google Translate, iTranslate, or DeepL. Rather, it integrates with the Google Translate API to provide automatic translations of online content for readers. As language learners read online, they can highlight words or phrases to get a translation in a pop up. Readlang provides language learners with a tool dedicated to intensive reading. By analyzing the text line by line, the learner can scrutinize each word and analyze the text in comparison to their own language. More experienced learners can benefit from this app as well by using it as a dictionary. By highlighting more words, they are provided with a translation of the term they are looking up in context.

Demonstrating the potential of MT

In order to demonstrate the potential of MT for language learners to produce the words and phrases that they need accurately and appropriately, we selected four common colloquial questions and phrases in order to identify the potential for mistranslation due to the inclusion of words with double meanings (How are you doing mate? How are you doing, mate? Let's catch up for a movie? Do you want to hang out after class?). We then tested these examples using Google Translate (powered by Google MT), iTranslate (powered by SYSTRAN MT) and DeepL (powered by DeepL MT). The results are presented and discussed in Table 29.2 below using back translations into English.

The results in Table 29.2 demonstrate that the more qualitative data the machine receives, the better the translated output. The machine will also specialize according to the data that are fed into it. Indeed, Zetzsche (2010) argues that the key concern for MT to produce successful output is that it is appropriately trained to handle different genres. We can therefore predict that MT will improve its output as more learners use MT and correct the resulting translations.

Table 29.2 Comparison of the translation of informal sentences with the three main MT engines and analysis of the results.

Google Translate iTranslate DeepL
1. How are you doing mate?
French Comment allez‐vous mate?
Back‐translation: How are you doing, mate?
Comment tu vas t'accoupler?
Back‐translation: How are you going to mate?
Comment ça va, mon pote?
Back‐translation: How are you doing mate?
Russian Как вы делаете помощника?
Back‐translation: How are you, assistant?
Как вы делаете мат?

Back‐translation: How do you obscenities?
Как поживаешь, приятель?
Back‐translation: How are you doing, friend?
Analysis:
Google Translate performed badly on the colloquial word “mate,” which was left in English in the French translation and translated using the wrong terminology in Russian. The register is also incorrect in Russian.
iTranslate performed poorly overall by translating the noun “mate” using the verb “to mate” in French and using a transliteration in Russian, resulting in a nonsensical sentence.
DeepL is the only engine which managed to produce a perfect translation in French and an adequate translation in Russian, with a slight shift in the register for the term “mate.”
2. How are you doing, mate?
NB: we have reused the original sentence and added a comma to see if a properly punctuated sentence would help the engine.
French Comment ça va, mec?

Back‐translation: How are you doing, mate?
Comment faites‐vous, mate?
Back‐translation: How are you doing this mate?
Comment ça va, mon pote?
Back‐translation: How are you doing, mate?
Russian Как дела, помощник?

Back‐translation: How are you doing, assistant?
Как вы делаете, мат?

Back‐translation: How do you obscenities?
Как поживаешь, приятель?
Back‐translation: How are you doing, friend?
Analysis:
Google Translate performed better with the addition of the comma. In French, the translation is now correct. In Russian, the register is now correct (use of the informal “you”); however, the terminology is still wrong. In this context, дружище or другaн would be better suited for the translation of “mate.”a
iTranslate does not provide better results for either language, even if the French translation now leads to fewer ambiguities.
DeepL generated the same translations for both languages.
3. Let's catch up for a movie.
French Rendons‐nous pour un film.
Back‐translation: Let's go to movie.
Nous allons rattraper pour un film.
Back‐translation: We are going to catch a movie up.
Allons voir un film.

Back‐translation: Let's go see a movie.
Russian Догоним фильм.
Back‐translation: Let's catch a movie.
Давaйте догнать для фильма.
Back‐translation: Let's reach for a movie.
Давай сходим в кино.
Back‐translation: Let's go to the movies.
Analysis:
Google Translate translates the meaning of the sentence with grammatical errors for both languages.
iTranslate uses the first meaning of the verb “catch up” for both languages, without taking into account that it should be used in conjunction with “movie,” resulting in nonsensical sentences.
DeepL is the only engine, which managed to produce a perfect translation for both languages.
4. Do you want to hang out after class?
French Voulez‐vous sortir après les cours?
Back‐translation: Do you want to hang out after class?
Vous voulez passer du temps après la classe?
Back‐translation: Do you want to spend time after class?
Tu veux sortir après les cours?
Back‐translation: Do you want to hang out after class?
Russian Вы хотите пообщаться после занятий?
Back‐translation: Do you want to touch base after class?
Вы хотите пойти после класса?
Back‐translation: Do you want to go after class?
Хочешь потусоваться после занятий?
Back‐translation: Do you want to hang out after class?
Analysis:
Google Translate translations for both languages are using the wrong register. Other than that, the French is correct and the Russian has a slight terminology issue. Both sentences would be understood by a native speaker.
iTranslate leaves out the main information of the sentence by not properly translating “hang out” for both languages.
DeepL is the only engine which managed to produce a perfect translation in French. The Russian translation is also correct, even if a slight variation in the Verb (потусить) is also possible.

a The concept of “mate” does not have a generic translation. It would generally differ depending on social contexts. The translations suggested here may therefore not always be appropriate.

Application of translation apps for language learning

Who are the language learners?

Understanding who the learners are and what their learning needs are is essential in identifying how they make use of and benefit from translation apps. According to François Yvon (2018), language learners use translation apps differently depending on the level of knowledge of their LOTE. Beginner learners such as the Nigerian students from the survey carried out by Elega and Özad (2017) resorted to using translation apps as a means of better integrating socially and to quickly solve language issues in social contexts. Additionally, in their survey of beginner Spanish language learners, Garcia and Pena (2011) also indicate that the learner would communicate more in the language they are learning when using a translation app. However, the question of whether this, in fact, leads to more learning or not remains.

There are two points to take into consideration when attempting to describe informal language learners. Firstly, all of the studies mentioned have examined language learning by university students, which encompass both formal and informal learning, but never informal learning on its own. Therefore, we would like to consider two types of existing language learners who we have called “academic” and “informal” language learners. One of the differences between academic language learners (those enrolled in a degree course) and informal language learners (those learning a language for their own benefit and on their own) that is clear from the Lai and Zheng study (2017) is the way each group uses mobile devices. Academic language learners tend to use mobile devices to provide help on the spot or to study anytime, anywhere (Lai and Zheng 2017). In contrast, informal language learners may be more inclined to use translation apps as a means of communicating more effectively with native speakers. This idea is prevalent in multiple language learner blogs.

The second element to take into consideration is the proficiency level of the language learners. The translation activities in language learning are usually associated with advanced learners and not proposed to beginners (Pym et al. 2013). Therefore, can translation apps still be useful for beginner and intermediate language learners? The next section, “How Do Language Learners Use Translation Apps,” focuses on beginner and intermediate language learners.

How do language learners use translation apps?

Current usage of mobile devices and apps for language learning is as varied as vocabulary and grammar learning, reading and understanding practices, pronunciation training, or comprehension (Bahrani 2011). However, the main aim of translation apps is to translate from one language into another.

Despite the fact that many foreign language educators are hesitant to allow Google Translate into their classrooms for fear that approving the use of translation tools might undermine the language acquisition (Groves and Mundt 2014, p. 119), the research already carried out undeniably demonstrates that translation apps are used for language learning outside the classroom. Students are in fact attracted to translation apps even when they are instructed not to use them (Jin and Deifell 2013).

In an attempt to identify how language learners utilize different technological tools to “construct self‐directed, out‐of‐class mobile learning experiences” the study by Lai and Zheng (2017, p. 302) revealed that language learners actively use mobile devices to support their learning. They examined how a group of university‐level, foreign language learners used mobile devices outside the classroom to complement their language learning. Bourne (2014) conducted a survey with university students to discover how language learners used and viewed technology for language learning. It appeared that most students were using online translation tools and thought that computers were helpful in their learning of the language.

Aside from these surveys, a multitude of language learners maintain blogs where they offer tips about learning more languages, faster. However, there is little talk about the use of translation apps to support their learning. One of these bloggers, Steve Kaufmann, asserts that his prolific learning of new languages in past years was possible thanks to technology. Google Translate, particularly, has played an important role in his language learning.

Using a list of skills required by language learners can help us identify how translation apps can assist learners of a foreign language develop these skills. In Table 29.3, we establish a list of skills necessary to the learner of a foreign language and associate them with how translation apps can support the development of these skills for language acquisition and usage. With this list, we will try to identify if translation apps can provide benefits to the language learner in areas other than translation, such as pronunciation improvement, grammar, analytical understanding of the language, vocabulary building, reading and understanding, and writing and composition.

Despite translation apps being originally designed to render phrases and sentences from one language into another, we have identified that this is not the only way they are being used. More and more, language learners use translation apps for various other purposes, the most common being as a bilingual dictionary. Indeed, language learners and translators alike often use translation apps as a dictionary to understand the meaning of a single word (Jiménez‐Crespo 2017). In a study about the use of mobile devices for language learning outside the classroom, Lai and Zheng (2017) have reported students using Google Translate to find the meaning of words, which were then used for in‐depth research and understanding of the word in context. This sophisticated practice is very similar to how professional translators would use such technology.

Benefits and drawbacks of using translation apps for language learning

Using translation apps for a purpose other than intended, i.e. the translation of sentences from one language into another can have some advantages. As we have discussed, some of the benefits of translation apps are that they are always available, with most of them now offering offline access to their engines from any smartphone. Language learners therefore get fast access to the information they are searching for and can use it straightaway. The convenience of these apps is supplemented by their many different usages. They are a pocket dictionary, a concordancer, and a tool to check your pronunciation and allow you to read, write, speak, and understand from anywhere. Moreover, the use of translation apps can generate incidental learning as a potential unforeseen advantage, whether it is extra vocabulary, the ability to search for synonyms or the requirement to be more discriminating as a learner or language user. The example in Figure 29.1 shows us the range of equivalents that Google Translate suggests which encourages the language learner to learn the importance of context when selecting terminology.

However, we should not overlook the drawbacks for language learners inherent in these technologies. We have to remember that MT systems, and consequently translation apps, were not designed as language‐learning tools (Niño 2008). They may therefore introduce errors that the language learner, especially the beginner, will have difficulty identifying. Novice language learners who have not mastered many of the intricacies of the language they are learning may not benefit as much from translation apps since they will be more likely to use them for casual language translation or communication needs. Therefore, the question that arises is whether MT is adequate for purpose.

Table 29.3 List of language‐learning skills in association with the features of translation apps.

Language‐learning skill Translation app support
Pronunciation Translation apps allow the learner to highlight a word and listen to its pronunciation.
The learner can speak to the translation apps to check their pronunciation. If the app properly spells out the word, the pronunciation is correct.
Grammar Translation apps are not currently able to support the teaching of grammar for language learners.
However, Google Translate, used as a web browser provides the grammatical class of the word that is looked up.
Analytical understanding of the language Post‐editing MT output exercises can be used to identify differences in grammar between the source and the target languages.
However, this exercise is better done by advanced learners.
Vocabulary building Learners can use translation apps to check the meaning of a term.
When selecting a term in the source or the target, translation apps provide a list of synonyms for the highlighted term as well as a probability level of usage for the given context (see Figure 29.1). This feature is useful for vocabulary building.
In their study on the use of online dictionaries for language learning, Jin and Deifell (2013) report that Google Translate was the second most popular tool used as an online dictionary. However, language learners would not fully rely on it due to its lack of contextual information.
The main reason for using a translation app for beginner learners was that it helped students look up words. In other words, they would use the app as a dictionary (Garcia and Pena 2011).
Readlang helps advanced learners building up vocabulary by saving flashcards of all the words that were looked up. These can be consulted at a later stage.
Reading and understanding As the learner is reading a text, they can search unknown terms/phrases with a translation app (e.g. Readlang).
Reading can be sped up by automatically looking up words in Google Translate, while reading.
Writing and composition As the learner composes text, they can check that their sentence structure and grammar is correct by checking the translation in their translation app.
This area is more likely to produce errors using a translation app.
Google Translate offers examples of word usage.
Screenshot displaying a range of potential translations for the highlighted term in Google Translate.

Figure 29.1 Display of a range of potential translations for the highlighted term in Google Translate.

Suggestions for research

Since the majority of language learners, whether learning formally or informally, appear to engage with translation apps in their language learning, a program of research is required to better understand the benefits and drawbacks of their use.

In the first instance, descriptive research should aim to provide a comprehensive overview of the field, including:

  • The learners. Who are they? Does age or language background, for example, determine whether learners use translation apps and how they use them?
  • The apps. Which apps are being used? Are they translation tools integrated into learning apps or are translation apps most frequently used on their own?
  • How the apps are being accessed and how they are being used. Are learners using them in conjunction with formal learning or a language learning app? Are they being used to replace language learning?

Such a research program will inform our understanding of the role of apps in learning.

In addition, empirical research is needed to examine whether translation apps make language learning more efficient or effective and whether learners acquire specific skills or not by using the apps, skills that they perhaps do not acquire through other methods. Are there ways of using the apps that render their learning more effective, for example?

Lastly, in view of the resurgence of interest in the use of translation as a language‐ learning strategy, we need to examine the relationship between the use of apps in informal learning and the practice of translation as it is used in the language classroom.

Conclusion

In this chapter, we have seen that translation apps are being used creatively by learners to enhance and facilitate their learning and language use. Beyond the use of translation apps as a convenient bilingual dictionary in their pocket, learners are using apps to facilitate communication where the requirements of the social and linguistic context in which they find themselves are beyond their current level of language ability. Translation apps undeniably support social interaction for immigrants, international students, and travelers.

We have also seen that current language learners are mostly digital natives (Jiménez‐Crespo 2016). Their familiarity with computers and the internet means that apps will almost always be the resource they consult before anything else. Moreover, they consider translation apps as a language‐learning tool (Bradley et al. 2017; Kaufmann 2015). However, as we demonstrated from our short comparison of translation apps, if the language learner hasn't reached a certain level of competence to critically evaluate the output and identify errors, the results obtained may be unusable. The less experienced the learner is with the language they are learning, the less likely they will be to identify the errors in the translation provided.

For language learning, translation apps are a medium for producing translations that are immediately useful; they have a specific goal for the learner, whether it is to understand a term or a sentence, to find out how a structure works in the foreign language, or to be able to communicate verbally. Considering they are a tool for personal, rather than commercial use, users generally tolerate inaccuracies and other non‐senses. But does this mean that they learn less or not as well through translation apps?

The fact that apps that have been designed for translation proper, have become integral to language learning adds weight to Cook's (2007) claims that translation has persisted principally in informal learning, where language learners follow their natural inclination to learn through their L1, the language they know. However, it is unlikely that apps support the acquisition of translation competence, the fifth skill, and therefore do not fulfill the promise of translation becoming the means to an end: translation (Cook 2007).

Finally, we conclude that translation apps can be used to support language learning but will not eliminate the need to acquire proficiency in the language the learner wants to communicate into. This idea is supported by Bourne (2014) who states that “technology has not yet mastered the art of translation, and consequently usurped the need for a person to actually learn a language.”

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Notes

  1. 1 “Intralingual” learning and teaching restricts learning to use of the language being acquired; “interlingual” uses the native language to support acquisition of the foreign language.
  2. 2 We have adopted Cook's (2010) use of the term bilingual to describe classrooms that allow or encourage the use of the students' native language in contrast to “monolingual” classrooms where the native language is discouraged or outlawed.
  3. 3 We can assume that manual translation (i.e. the process whereby a translator types their translation from scratch without the use of computer-assisted translation tools) is not practiced anymore since nearly all translations are now produced using a word processor rather than being handwritten.
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