Long gone are the days of using a bilingual dictionary to translate from one language into another.
Today, you can find words and phrases in foreign languages online. You can use an online translator tool to get a translation within minutes. Google Translate is able to translate more than 100 million words per day using machine translation (MT).
Machine translation is not only for personal use but also helps businesses and companies reach a global audience. Machine translation can be used to translate website content into many languages, eliminating any language barriers. This allows them to reach new markets and gives marginalized groups more information.
Machine translation (MT), is an automated type of translation where a computer does the translating without the help of a human translator. An algorithm can be used to translate text between one language (the source language) and another language (the target language).
This algorithm must be trained with data samples. Data samples can be either generic or specialized. Google Translate is an example of a generic machine translator engine. It’s not designed to be trained with data samples that pertain to a particular domain. The platform collects more data as more users use it, which means that the algorithm and output of the engine improve.
Instead, specialized machine-training engines are equipped with specific data sets and are continuously fine-tuned to ensure a more precise output.
There are many types of MTs. Here are the four most common:
1. Rules-based Machine Translation
This type is based on rules that were developed in collaboration with language experts. They are based on grammar rules and dictionaries as well as semantic patterns.
2. Statistical machine translation
This type uses algorithms to analyze text samples that have been translated to create a database of translations. The database is organized according to how likely it is that one word or phrase from the source language will match another word or phrase from the target language.
3. Syntax-based machine Translation
This type converts syntactic units to words. This subtype of statistical translation is called the “syntax-based machine translation”.
4. Neural machine translation
This type blends statistical machine translation with the neural network. This type is the most complicated, but also the most powerful.
There are also hybrid machine translation systems, which combine multiple approaches.
Neural Machine Translation
We have already discussed that neural machine translation is a more advanced version of statistical machine transcription. To predict long sentences and phrases, it uses an artificial neural network. NMT uses less memory than statistical translation because the models are trained together to optimize translation quality.
These networks automatically adjust their parameters to improve quality and compare the output to the expected translation during training. During this phase, which involves large data sets, humans must train them.
NMT uses deep learning and artificial intelligence to achieve the best machine translation.
NMTs can be described as any machine translation that relies on an artificial neural network to predict a sequence number. Let’s assume you have a sentence in English that needs to be translated into German.
It could be, “I drink too much coffee.” Each word corresponds to a number.
The network will interpret the sentence as a sequence number and locate the sequence in the target language. Users will receive “Ich trinke zu viel Kaffee” as an output or the answer.
How does the network convert one sequence of numbers to another? It uses a complex mathematical formula.
The first sentence becomes a string that runs through the formula, and then it is transformed into another string.
This process is repeated millions of times, which means that millions of sentences in English are converted into strings of numbers. The strings of numbers are then translated into the corresponding strings of German numbers. Each sentence is a learning opportunity for the neural network. It changes little and then refines its parameters through back-propagation.
Statistical Machine Translation can also convert phrases into strings of numbers, but it doesn’t assign relationships between words like neural networks. A neural network will assign numerical values to two words that have similar use cases if it receives data samples. The neural network will assign close numerical values to words such as “but” or “however” if they are found in data samples.
These networks consider the context of every sentence. These networks analyze the order of the sentences and the words within them. They are able to communicate fluently with higher levels of accuracy.
A Brief History of Translation Technology
Al-Kindi, an Arabic cryptographer, invented some of the methods we use in translation technology today. However, translation technology wasn’t developed until the middle of the 20th century when computers were more affordable and easier to access.
In the 1950s, IBM and Georgetown University created the first machine translation system (MT). The system was rule-based, and it used preprogrammed rules and dictionaries. It was slow and unreliable by today’s standards. However, it was revolutionary back then and paved the way for advancements in MT.
Voice-to-text technology was first developed by DARPA and the US Department of Defense in 1970.
Electronic dictionaries and terminological databases were introduced in the 1980s. The ALP System was developed by Coventry Lanchester Polytechnic University and included concepts that would become the current translation management system (TMS).
IBM researchers had already developed statistical machine translation by the start of the 1990s. More commercial computer-assisted translator tools were available. IBM released a new version in the late 1990s of its statistical translator engine that was phrase-based instead of word-based. It was the market standard for many years, until Google’s neural-machine translation (NMT), technology arrived in 2016.
Google Translate was launched in 2006 by Google. It used statistical algorithms and predictive algorithms that were based on sentences and words it had learned previously. Many times, the output was full of grammatical errors.