From Classroom to Real World: How Machine Translation is Changing the Landscape of Foreign Language Learning

In today’s fast-paced, interconnected world, being able to communicate in multiple languages is more important than ever. But learning a new language can be very challenging, especially when it comes to using and understanding foreign language materials. 

Enter machine translation (MT): a powerful tool that can definitely help bridge the gap between the classroom and the real world. In this article, we will explore the history in which MT has impacted the field of language education and its role in bettering the ability of learners to translate and learn a new language.

Machine translation tools like Google Translate can be very useful for language learners and teachers, but they can also have its pitfalls. In this article, we will take a look at how these tools are used and suggest ways where we can make the most out of them. The main goal is to find mindful ways we can use this technology in learning a new language.

A Brief History of Machine Translation

Machine translation is a technology that uses software to translate text from one language to a target language. The technology has been around for a very long time with the first machine-translated texts happening around the 1950s where an IBM computer translated Russian sentences into English, developing the first rules-based machine translation system. These machines weren’t that reliable and researchers had to create a lot of rules for transformation and structure. This hardly solved the unreliability that much because it simply wasn’t possible to come up with every rule.

But it was all uphill from there. The technology has advanced so much over time. From statistical machine translation in the 1980s to the Neural Machine Translation in the 2000s, machine translation has become so easily accessible to anyone with an internet connection or mobile data. From getting quick translations with Google Translate, to having automatic translations available on social media, machine translation has become such an important tool for taking in and learning information.If you’d like to go in depth with the history of Machine Translation, here’s an article we wrote all about it.

MT in the Classroom

Translation, as part of learning a language, has had a bad rep. Many people associate it with the traditional Grammar Translation Method, which focuses on translating source texts into the target language and vice-versa. This approach has long been outdated and was replaced by more interactive methods in the 60s. However, some experts argue that with the right rules, translation can be a valuable skill for advanced learners as they continue to master a new language. Some argue that translation should be as important as learning how to speak, listen, read, and write in a new language (Campbell, 2002). But it’s important to note that this approach is not for everyone, and particularly not meant for beginners.

Artificial Intelligence is making big waves in our daily lives, including the way we learn a language. Technology has always been seen as a useful tool for learning languages, and machine translation is no exception. These systems can produce large amounts of translation in such a short time and can quickly update with the latest words and terminologies. On top of that, some models can provide spelling and grammar checking. We simply can’t ignore how useful this technology is, especially in learning a language. Let’s dive deep into the history of MT in language learning.

Many experts in the field of language learning have been exploring the benefits of using machine translation tools as a way to aid language learners. Studies have been conducted on the use of these tools as a form of computer-assisted language learning (CALL) since the 80s, with Harold Somer (2003) and Ana Niño (2008) providing valuable insights into this field.

Early studies on MT in language learning had a bad view on the technology because of the inconsistent output it produced giving students misleading phrases of the target language. However, it was also suggested that it could help learners contrast grammatical differences between languages and develop revision techniques (Higgins, 1984). The view that MT is a bad model for learning languages  continues to dissipate as authors suggest that it could also train learners in revising text (French, 1991).

In the article “Are Machine Translation Tools a Threat to English Teaching?” Dorothy Zemach examines the benefits and disadvantages of having MT in a classroom setting. She notes that while some teachers allow machine translation to be used as a tool for language learning, others are concerned that it may not effectively help students learn the language. To counter this, these language teachers prefer to have students practice writing sentences and communicating in the target language during class time.

Today, the most commonly used MT models among students are Google Translate and DeepL. Google Translate, being the most accessible, is nevertheless found to be the least reliable when it comes to longer texts. Patrick Conaway, associate professor of English at Shokei Gakuin University in Japan, said “Well, I can tell if they’re using Google Translate, but if they’re using DeepL, I can’t tell.” This may be due to the fact that Google Translate previously relied heavily on statistical machine translation, which means it prioritized preexisting data rather than grammatical rules. In 2016, the company integrated neural machine translation into its automatic translation tool, which has since given better results for longer texts. However, Google Translate is still best for translating one word into another rather than any larger texts.

When it comes to idiomatic expressions, DeepL is the preferred choice. Patrick Conaway says that DeepL translation isn’t entirely flawless, but that it produces grammatical writing comparable to that of a high or intermediate-level student. But this does not mean the tool is completely void of detection. Students who depend too heavily on it can still get caught because DeepL adds “Translated with www.DeepL.com /Translator (free version)” at the end of every generated text.

Could MT Replace Language Teachers?

Some teachers may have a negative impression of machine translation because the tool can be seen as a threat to their careers. While MT is a very useful tool, it's not a replacement for learning a language. But it may change the language learning industry by diminishing the need for short-term language classes with the rising popularity of machine learning apps like Duolingo giving interactive language learning exercises with just a few clicks away.

Benefits of Using MT in Language Learning

Machine translation has a lot of benefits when it comes to language learning. Let’s talk about them.

Development of practical skills

- With the ability to quickly and easily translate text, students can use MT to practice reading, writing, and speaking in their target language. For example, it can be used as a reading comprehension tool by allowing readers to easily look up words they do not understand and even create personal glossaries of the target and source words. This can really help students improve and nurture their confidence and fluency in the language, which is so important when it comes to real-life interactions.

Improve access to low-resource language for everyone

- With the ability to translate text, non-native speakers can access a wealth of language resources that would otherwise be unavailable to them. This includes everything from news articles and literature to online content and social media, which can greatly aid their language learning journey.

Motivate language learners during their journey of language acquisition

- Machine translation can help language learners approach real-life texts like news, song lyrics, and social media posts  that are difficult to understand but provide rich cultural insight. This can lead to a greater appreciation of the language itself and motivate individuals to focus on more practical language-learning approaches.

Challenges of Using MT in Language Learning

While MT can be a powerful and useful tool for language learning, it’s not without its challenges.

Ensuring the quality and accuracy of translations

- MT is still a developing technology and it definitely has its flaws. It may not always  give accurate translations and can misinterpret cultural references and idiomatic expressions. This misinterpretation can be transferred to a student, who has then incorrectly learned certain information and may apply it practice.

Potential for students to depend too much on translation tools

- The convenience of this technology can be addictive and can lead students to become too dependent on the technology. This may prevent them from developing confidence in speaking a new language. This is detrimental to language learning because developing the ability to understand and use the target language in its natural context is crucial to language improvement.

Potential for replacing human interaction and feedback

- MT’s convenience of instant translations makes it easy for students to bypass human interaction and avoid the valuable feedback that comes with it. This can lead to a noticeable lack of fluency and a lack of confidence in the target language. Individuals who are completely dependent on MT may therefore only communicate in very limited contexts, which is the opposite of the aims of learning a language.

Reinforce students’ native language biases

- By providing translations in the students’ native language, MT can make it easy for students to rely on their native language to understand the target language. This can lead to a lack of actual understanding of the target language and its culture, which is essential for fluency and cultural competence.

From this, we can say that using MT can be a great way to enhance language learning, but it’s important to keep in mind that it also comes with its own set of pitfalls. Therefore, we must make sure we use the tool in a way that complements and improves the language learning process, rather than replacing it. By providing guidance and support to learners, we can help them navigate the challenges that can arise with MT in order to make the most of this technology.

Conclusion

To sum things up, machine translation is an undeniable presence in language learning and a powerful tool that can help individuals understand and communicate in a foreign language. While some educators may be skeptical of its use, it has the potential to supplement traditional language learning methods and make language acquisition more accessible to a much wider range of learners. However, it is important to recognize that it is not a replacement for traditional language learning methods and it should be used in conjunction with other tools and strategies.

In the future, language learning with machine translation will likely become more sophisticated and integrated into classroom instruction, providing new opportunities for learners to improve their language skills in a way that is both efficient and effective. With the integration of machine translation, language learning will become more convenient, accessible, and interactive. Therefore, it is important for educators to stay up to date with the latest developments in machine translation technology and find ways to effectively integrate it into their language instruction.

Bibliography

1. Campbell, S. (2002).  Translation in the Context of EFL-The Fifth Macro shill?. TELFIN Journal, 13(1), 58-72.

2. Somers, H. (2003). Machine translation in the classroom. In H. Somers (Ed.) Computers and translation. A translator’s guide (pp. 319-340) Amsterdam/Philadelphia: Benjamins.

3. Niñ o, A. (2008). Evaluating the use of machine translation post-editing in the foreign language class. Computer Assisted Language Learning, 21(1), 29-49.

4. Higgins, J., & Johns, T. (1984). Computers in language learning. Aylesbury: Adison-Wesley.

5. French, R.J. (1991). Machine translation. In W. Brierley I.R. Kemble (Eds.), Computers as a tool in language learning (pp. 55-69). Chichester: Ellis Horwood.

6. Kliffer, M. (2005). An experiment in MT post-editing by a class of intermediate/advanced French majors. In proceedings EAMT 10th Annual Conference (pp. 160-165).