All of us are familiar with popular machine translation programs like Google Translate. Whether we need to quickly translate a website or look up a word when we’re with somebody who doesn’t speak our language, the ease and availability of these programs can’t be beat. That being said, its accuracy is commonly called into question. Typically mistranslations are humorous, but if they are published or printed on behalf of a brand, the results can be misleading and damaging to a company’s image. Neural Machine Translation aims to change all that…
Problems with Direct Machine Translation
When “just the gist” of the meaning won’t do for what you are translating, professional translators and localization services are vital to preserving the integrity of a brand. Most existing automatic translation services rely on statistical machine translation, which simply chooses the translation that is most likely to be correct based on analysis of both original and translated versions of a given text. This method has improved over time with a more sophisticated type of program, known as neural machine translation, gaining traction with highly influential companies like Google and Microsoft.
Neural Machine Translation
In recent decades, computer scientists and software developers have been making strides towards creating computers that simulate human learning, like those that utilize neural networks as in Neural Machine Translation. In an artificial neural network, artificial neurons (or units) are arranged in layers. These layers are made up of three types of units: Input, Output and Hidden units. Input units take in and process information from the outside world, and output units signal how to respond based on learned information. Between the input and output units are layer(s) of hidden units, which convert the learned information into a response. In most networks, hidden and output units are connected to units in adjacent layers. Each connection has a weight that represents its strength. Stronger connections exist between units that are more likely to lead to the correct output. Input units detect information presented to the system and then activate the most relevant hidden units. These hidden units then activate the appropriate output units to generate the most suitable response to the original stimulus.
Modifying the Process
This process of learning is similar to a human learning a new skill. For example, when a person first serves a ball in tennis and it goes too far, they will change the angle of the racket and the force they hit the ball with. In doing this they are recognizing what they did wrong initially, making modifications, and performing better the second time. In an artificial neural network a comparable process takes place. The actual and expected outputs from a stimulus are compared, and the weights between the units are modified accordingly. It is important to note that the computer is not learning the way we learn; it is simply responding to inputs and adjusting strengths between units to eventually produce the desired output.
The Future of Machine Translation
What does Neural Machine Translation mean for machine translation in general? Language is constantly changing, and employing a more adaptable system that will lead to more accurate and relevant translations with continued use. One of the biggest changes that users will be able to see is an improved translation quality for slang; the networks will register slang and colloquial language and be able to better preserve the tone and content of the original message (which is currently something only a certified translator should be hired to do). Artificial neural networks have a long way to go before they are reliable enough to use for important documents. Certified translators with comprehensive knowledge of the original and target languages, as well as the subject of the translated material, are still required to get accurate and professional translation services.
About Language Connections:
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