Neural Machine Translation plays a salient role in practice since the process of generating the output takes only a few seconds. Thus, it benefits practitioners as the demand for translation is growing to meet the needs of today's industry. However, is it effective when compared to how localization works? Let’s deep dive to know more!

The History of Neural Machine Translation

How did this innovation transform from such a trial to an influential support tool for translation?

It all started in 1932 when George Artsrouni proposed the concept of a “mechanical brain.” At that moment, he envisioned its fundamental use for translating foreign languages. He then intended it to function toward a multilingual dictionary. Two decades later, during the 1954 Georgetown-IBM experiment in New York, the public witnessed an automated language translation for the first time. It was capable of converting brief messages on certain topics, such as chemistry, politics, law, and military affairs, with Russian to English reference.

The reaction of the audience toward the collaboration of scientists and engineers was likely a mix of astonishment as well as wonder upon learning that a computer had just performed real-time language translation. Charles Babbage's (a British mathematician who first put forward the idea of ​​computers) visionary ideas didn't seem so far-fetched after all as the story didn’t end there.

Machine Translation has undergone salient transformation throughout its development. It has progressed from systems that demanded hours or even days of computing time to produce translations of questionable quality. Today, it is known as Neural Machine Translation systems that can process the same content in a matter of seconds.

Get Closer to Neural Machine Translation

Neural Machine Translation represents an end-to-end learning approach for automated translation delivery. In this method, a neural network within the program takes on the roles of both encoding and decoding the source text, as opposed to relying on predefined rules from the outset.

Consequently, NMT has the potential to address numerous issues associated with conventional phrase-based translation systems and has demonstrated its ability to generate translations of higher quality.

Exhaustively, the mechanical process in NMT utilizes artificial intelligence (AI) to autonomously convert content from one language into another without human involvement. In simpler terms, a computer program performs translations without the need for human translators to step in.

Although initial experiments with automatic translation date back to the early 1950s, it wasn’t until the early 2000s, when statistical methods were employed, that machine translation truly gained momentum.

In its early stages, the quality of translation was quite rudimentary, and training the machines demanded extensive labor. In contrast to contemporary deep learning techniques, which harness the power of artificial intelligence, earlier machine translation models required developers to manually establish and program what essentially amounted to a substantial set of regulations.

Machine Translation vs Neural Machine Translation

These two may overlap each other as they refer to the technology based. However, discovering the primary subcategories of machine translation is beneficial to enhance the understanding. What are they?

Rule-based machine translation

This kind of machine translation is mostly outdated as it counts on linguistic knowledge of both the source and target text. Human linguists take an initial preparation to create rules based on grammar structures, word order, and phraseology for processing the data. These regulations guide the process by mapping each source text input to an appropriate translation in the target text after consulting dictionaries.

Statistical machine translation

Statistical models operate by examining extensive volumes of pre-existing translations and multilingual datasets, then searching for statistical patterns within this data. These patterns enable the users to formulate hypotheses to translate similarly structured texts hereafter. Meanwhile, training these models requires large resources to specialize the input for a particular domain. The results can be quite satisfactory, particularly when dealing with technical or scientific texts. Besides, statistical translation models were indeed word-based, but they later evolved into phrase-based systems that consider word context.

On the other hand, neural network models differ significantly from phrase-based systems. While it divides sentences into individual words and phrases, neural networks consider the entire input as a whole at each step when generating the output.

Google Translate transitioned to Neural Machine Translation in 2016 to drive its Google Neural Machine Translation system (GNMT). Google made this innovation with the intention of reducing the necessity for complex engineering and design decisions, while simultaneously enhancing its quality and velocity.

How Does Neural Machine Translation Work?

Neural Machine Translation employs neural networks for the task of translating source text into target text. Neural networks are highly adaptable to large datasets and can operate with minimal quality control. In this process, the system comprises two main components: an encoder network and a decoder network, both of which are implemented as neural networks.

In general, a neural network indicates interconnected nodes, which are inspired by the human brain. It functions as an information processing system where the input goes over these interconnected nodes to generate output.

Specifically, the neural network architecture is often referred to as a “sequence-to-sequence” (Seq2Seq). What does it mean? This architecture works by taking one source text sentence and generating a correlating target text sentence, making it well-suited for translation tasks.

Distinguish the Benefits of Neural Machine Translation

The potential of Neural Machine Translation is rooted in its neural network architecture, allowing it to learn from extensive data and reshape to new frameworks. This technology is particularly well-suited for clients requiring rapid, precise, and flexible content. All in all, it benefit can be listed as follows:

High Accuracy

Its utilization of vast datasets and language modeling, comprehends the broader context of words and phrases. As a result, it proceeds with more precise and fluent translations that improve time by time. It then differs from traditional phrase-based machine translation by considering the immediate context of a few words surrounding the translated word.

Fast Learning

Neural networks can be customized in a short period through automated processes by eliminating manual methods in rule-based machine translation.

Simple Integration and Flexibility

NMT inherits from its statistical principle, while it has the ability to be seamlessly integrated into software applications via APIs and SDKs, so it can be applied to various content file formats.

Customization

NMT systems are typically customizable, allowing for the fine-tuning of output through terminology databases, brand-specific glossaries, and other data sources to enhance translation quality.

Cost Efficiency

Human translation engages with high-budget effort. Moreover, when it comes to projects involving a large volume of words and multiple languages. NMT provides a cost-effective alternative, while delivering high quality with quick output. However, human translators still involve post-editing when necessary.

Scalability

NMT offers the ability to match with its demands in industry, so it makes a valuable tool for scalable projects.

How Does Localization Work?

Localization works by adapting content to convey equivalent meaning in another region. It encompasses more than just translation, including adjustments to associated images and cultural elements that influence how the content is recognized.

The essence of localization lies in making the content appear as though it was originally crafted with the target audience. A successful one resonates with the audience since it incorporates cultural nuances that adopt the same feeling.

Meanwhile, localization encompasses the content elements to execute, such as:

Language

Translation is a fundamental part of localization, but it’s only the early step to be done. It then involves converting content from the source text to the target text.

Tone and Message

A well standard localized product must be emotionally connected with the target audience as technically accurate isn’t enough. It takes into account whether the content aims to persuade or educate the audience, especially in marketing materials.

Imagery and Color

The acceptable thing in one country can be offensive in another. For instance, cultural interpretations of gestures and symbols, like the thumbs-up sign, can vary significantly.

Furthermore, localization can become a comprehensive activity to snatch unique dialects of a specific region within a country or even the same language. For instance, in both the U.S. and the U.K, they differ in spelling (localization vs. localisation) and vocabulary choices (sweater vs. jumper).

When Localization Works?

Incorporating localization early in translation products is advantageous as it simplifies the overall task. However, for a successful one, internalization turns into an essential process as well. This foundational process is often mistakenly grouped with translation and localization, but it serves as the technical groundwork for these processes. Internationalization involves preparing your codebase to support multiple languages and formats effectively.

When expanding a product globally, it’s essential to incorporate internationalization from the outset of the development process. This approach ensures that the offering delivers a consistent and user-friendly experience across all languages and regions, making it easier to adapt and localize content as needed.

Neural Machine Translation toward Localization

Translation and localization are closely related but distinct processes. While translation is a fundamental aspect of localization, they are not the same thing. Translation involves reconstructing a text from one language into another, while preserving the original text and communication. Thus, there are two main approaches to translation:

Machine Translation

This method offers a quick and cost-effective way to translate content between different language pairs. It can provide results in minutes or even seconds, but it may lack the nuanced understanding of context and culture.

Professional Translators

Human translators provide high-quality translations with a deep understanding of cultural nuances. They are often preferred for complex or specialized projects, such as international SEO optimization or creating subtitles for videos.

Localization, on the other hand, goes beyond mere translation. It considers the culture of a specific region and the related nuances in language that are influenced by that culture. Localization ensures that the content not only reads correctly in the target language, but also feels culturally appropriate and relatable to the local audience. It encompasses various elements, including adapting the layout, addressing cultural references, and aligning content with regional preferences.

In essence, while translation focuses on converting written words from one language to another, localization takes it a step further by considering the broader cultural context and nuances in language that are specific to the target region.

How to Choose a Suitable Localization Agency?

Localization comes with its fair share of challenges. The task of translating and adapting the content can appear to be a daunting endeavor. Without the assistance of a suitable localization agency, it may be struggling to gain insights into the localization credibility.

An adequate localization agency empowers content to tailor their localization workflows, ensuring they are efficient and effective for target audience in respective steps:

A localization agency seamlessly integrates with the content management system, automatically parsing and forwarding any content updates or new material to be translated.

Assigned translators handle the translation work, keeping track of their progress and directly communicating with clients if any questions arise.

Once the translations are ready, they are automatically integrated back into the client’s CMS, ready for the publishing step.

In Conclusion

While technology like Neural Machine Translation offers numerous advantages, the human touch remains essential to achieve optimal results. Initially, humans need to process this tool to generate a database used in subsequent projects. Moreover, how the localization works remains highly dynamic, demanding in-depth research into the target culture, a task beyond the capabilities of machines. Let’s take a look from the examples below:

While you’re likely familiar with McDonald’s slogan, “I’m lovin’ it," it’s interesting to note that in China, the translation becomes “I just like it.” This adaptation stems from the Chinese language’s deeper connotations of the word ‘love,’ which are seldom expressed directly.

Taking another example from KFC, their slogan “Finger lickin’ good” underwent a change in China, becoming “Eat your fingers off.” This alteration can be attributed to cultural sensitivities and taboos.

Is it effective for using Neural Machine Translation in localization? Sure. However, a capable translator still needs to do supervision for cultural things that it’s beyond machine capacity that should be trained before by inputting the database.