It was only in the early 2000s that computer software, storage technology and hardware started to meet the requirements of machine translation. At the early stages of development, statistical language databases were used to teach computers how to translate text. This required a lot of manual work and time. For each new language they had to develop a new database. Since then, machine translation has become faster and more accurate, and several different machine translation strategies have emerged.
For this approach, linguistic experts have developed built-in linguistic rules and bilingual dictionaries for specific sectors or topics. Rule-based machine translation uses these dictionaries to accurately translate specific content.
Neural machine translation uses artificial intelligence technologies to train languages and continuously improve this knowledge using neural networks, a special machine learning technique. It is often used in conjunction with statistical translation techniques.
A neural network is a set of interconnected nodes resembling the human brain. It is an information system in which input data passes through several interconnected nodes to create output data. Neural machine translation software uses neural networks to handle huge data sets. Each node performs a single attribute change to the source text to convert it to the target text until the output node produces the final result.
Neural networks take full account of the source sentence at each step in creating the output sentence. Other machine translation models break down the input sentence into sets of words and phrases, matching them with a word or sentence of the target language. Neural machine translation systems can address many of the limitations of other methods, and often provide higher quality translations.