How does neural machine translation works?

Neural Machine Translation is a fully-automated translation technology that uses neural networks. NMT provides more accurate translation by accounting the context in which a word is used, rather than just translating each individual word on its own.

Is machine translation better than human translation?

Human translation Over the past few years, machine translators have become more accurate with the programs learning more about words and their content. This has made machine translation and automatic translation services become some of the driving forces behind the development of this technology.

How does a language translator work?

Translators work with the written word, converting text from a source language into a target language. This is far more than replacing one word with another. Highly specialized content may require the translator to retain elements of the source language culture in the target language translation.

What are the main components of neural machine translation systems?

Almost all neural machine translation models employ the encoder-decoder framework (Cho et al., 2014a). The encoder-decoder framework consists of four basic components: the embedding layers, the encoder and decoder networks, and the classification layer.

Which language doesn’t have to be translated for the computer to understand?

Some examples of high-level languages are FORTRAN, COBOL, Basic, Pascal, C, C++, Java, and so on. A computer does not understand any language other than machine language, so it needs a translator which converts assembly language and high-level language programs into machine language.

Why is SMT better than NMT?

Data Quality NMT requires higher quality training data than SMT. Once an NMT engine has been trained, if bad data is found, the entire engine must be retrained to remove the bad data. More data can be added with an incremental training to overpower the flawed data, but this is not always practical.