Understanding Machine Translation (MT): Types, Capabilities, and Future

Before the inception of remarkable modern technologies, the global market was limited by language barriers which restricted the success of many businesses. While there are diverse languages across the nations of the globe, people from different races and ethnicities often find it difficult to interact with each other and execute certain transactions together, depriving them of several opportunities.

Nonetheless, the challenges that come with these language differences have called for the need to build an infrastructure that allows languages to be translated and facilitates the process of communication. This idea has been brought to reality through machine translation (MT) which allows you to leverage computer applications to interpret languages.

To help you grasp this concept better, here is a guide that will help you understand machine translation and describe its types, benefits, challenges, and lots more.

Machine Translation Explained

Machine translation can simply be defined as the process of automatically translating text or speech from one language into another using computer applications. The sole aim of this technology is to unify speakers of different languages together, allowing them to seamlessly communicate with each other with little or no barriers.

Due to its design, machine translation features a system that takes the text in one language and converts it into another language while keeping the meaning and context as accurate as possible for its audience to understand. It employs advanced algorithms and machine learning to automatically convert text or speech from one language to another. This process generally involves preparing the input text or speech by cleaning and organizing it.

Thus, the machine translation system is trained using various examples of texts in multiple languages and their corresponding translations. It learns patterns and probabilities of how words and phrases are translated from these examples. When you input new text for translation, the system uses what it has learned to generate the translation. In some cases, additional adjustments may be made to refine the results if necessary.

While the machine translation system is trained via the data inputted into them over time, the data it is using can be either generic data, which is knowledge from all past translations, making them versatile for different applications, or custom data, where specific subject matter expertise is added to the engine, like in engineering or other specialized fields. Users can utilize either of the data depending on their needs.

Machine Translation: Brief History

The history of machine translation dates back to the 1950s when early computer scientists attempted to use computing power for language translation. However, the task’s complexity exceeded their expectations, and early machines lacked the necessary processing power and storage.

It wasn’t until the early 2000s that software, data, and hardware reached a level where basic machine translation became possible. Developers used statistical language databases to teach computer translation, a process that required considerable manual effort.

Notably, the 2010s marked a significant breakthrough with the rise of neural machine translation, introducing deep learning techniques and neural networks to translation models. Google’s “Google Neural Machine Translation” (GNMT) system in 2016 represented a pivotal moment in this technology’s development.

Machine Translation Types

While the technology behind machine translation systems has advanced significantly in recent years, it has adopted three primary approaches to automatically translate text or speech from one language into another. These approaches include rule-based machine translation (RBMT), statistical machine translation (SMT), and neural machine translation (NMT).

Rule-Based Machine Translation (RBMT)

Rule-based machine translation (RBMT) was an early approach to translation using predefined linguistic rules. It had low-quality output, required manual addition of languages, and significant human editing. RBMT relies on linguistic experts to create rules for source and target languages, resulting in grammatically accurate but often overly literal translations. While RBMT is precise for languages with strict rules, it struggles with context and nuance, leading to less natural translations. Developing and maintaining rules for various languages is labor-intensive, especially for languages with complex grammar. Additionally, RBMT may struggle with ambiguous phrases or words in the source text. This traditional method is rarely used today due to these limitations.

Statistical Machine Translation (SMT)

Statistical machine translation (SMT) uses statistical models to understand the relationships between words, phrases, and sentences in a text and then applies this knowledge to translate it into another language. While it’s an improvement over rule-based MT, it still has some of the same issues. SMT is being replaced by neural MT but is occasionally used for older machine translation systems. It stands out from RBMT as it doesn’t rely on predefined rules but learns from large bilingual text collections to make translation decisions. However, SMT has its limitations, such as being reliant on the availability and quality of parallel text data, struggling with context, and potentially generating less fluent or contextually accurate translations, especially for less common phrases.

Neural Machine Translation (NMT)

Neural machine translation (NMT) represents a modern approach to automated translation, leveraging artificial intelligence to mimic the continuous learning of human neural networks. Unlike older rule-based or statistical methods, NMT’s neural networks are responsible for encoding and decoding the source text. NMT is the prevailing standard in machine translation due to its superior accuracy, scalability to multiple languages, and faster performance once trained. It excels in capturing context and delivering fluent, contextually accurate translations. Nevertheless, NMT does have limitations. Its performance relies on the availability of large, high-quality parallel corpora for training. Additionally, training and deploying NMT models can be computationally intensive, often necessitating powerful hardware like GPUs or TPUs.

Automated vs Machine Translation

Let’s clarify the distinction between automated translation and machine translation, as they often get mixed up, however, they perform different roles.

Automated translation involves incorporating features into computer-assisted translation tools (CAT tools) or cloud translation management systems (TMS) to automate manual or repetitive translation-related tasks. Its purpose is to streamline the overall translation process, improving efficiency. For instance, automated translation might initiate machine translation for a portion of the text as one of the many steps in a translation workflow.

On the other hand, machine translation is all about using software to convert text from one natural language to another without any human involvement, unlike traditional translation. This is why it’s also referred to as automatic translation.

Capabilities and Challenges

Over the years, machine translation’s speed and volume capabilities have seen remarkable enhancements due to ongoing improvements in machine learning algorithms and hardware technology. It can now translate millions of words almost instantaneously and continues to get better as more content is translated. For high-volume projects, MT not only handles volume at speed but can also integrate with other software platforms like content or translation management systems to maintain organization and context during translation.

Moreover, MT’s improved accessibility, offering translations in multiple languages, benefits both businesses and customers by eliminating language barriers and enhancing the customer experience. This expansion to a wider audience helps businesses grow their market share.

Another advantage of MT is cost reduction. While human translators still play a role in refining translations to match the original content’s intent and localize it per region, MT does the initial heavy lifting, saving time and costs, even when post-editing by human translators is involved.

Nonetheless, while machine translation is a cost-effective and quick solution for global expansion, it’s important to recognize the challenges it presents. These challenges include:

• Accuracy and domain specificity. MT can struggle with precise domain-specific terminology and context, often producing translations that lack the depth of understanding that human experts can provide.
• Linguistic nuances. MT may miss subtle linguistic nuances, cultural references, or idiomatic expressions, which are crucial for conveying meaning accurately and effectively.
• Low-resource languages. MT is less effective for languages with limited available training data, as it relies on extensive bilingual corpora for training.
• Machine translation post-editing. To address issues with MT quality, businesses often employ human post-editors to refine the translations. This adds a layer of cost and time.
• Privacy. When sensitive or confidential information is involved, relying solely on MT can pose privacy risks. Human involvement is necessary to maintain data security and confidentiality.

Future of Machine Translation: Will It Replace Humans?

Translation technology has made significant advancements, but it’s unlikely to completely replace human translators. While machine translation tools like neural machine translation (NMT) are proficient in handling straightforward, repetitive tasks and providing quick translations, they still struggle with context, nuance, and understanding the cultural and linguistic subtleties that human translators excel at.

Human translators bring cultural and contextual insights to their work, ensuring that translations are accurate, idiomatic, and sensitive to the nuances of the source and target languages. They are indispensable in complex or specialized fields like legal, medical, or creative content, where precise and culturally appropriate translations are critical.

Machine Translation Engines

The main providers of generic machine translation engines include Google Translate, Microsoft Translator, DeepL, and IBM Language Translator. These providers offer pre-trained models for a wide range of languages and general translation needs.

For custom machine translation engines, there are specialized companies like Lilt and Iconic Translation Machines that offer tailored solutions for specific industries or organizations.

Final Thoughts

Machine translation (MT) has added lots of value to the global space, eradicating barriers to language differences while allowing people to seamlessly access translations to languages they do not understand.

While this has greatly impacted businesses worldwide, especially those that hold international deals, it has also impacted the social life of many as it tends to strengthen relationships among people of different languages.

As this technology continues to evolve, the world will soon overcome every limitation that tends to come with language barriers using efficient computer tools.