The conversion from sentence to vector, called the Vector Mapper, is the first part of the network. Vectors and matrices are an assortment of numbers representing data. In order for this input to be interpreted by a neural network, it must be converted into a format it understands, i.e. To continue our example of English to French translation, a neural network takes an English sentence or sequence of words as an input and gives a French sentence or sequence as an output. Neural networks are taught language patterns and eventually are able to translate a given English sentence into French all on their own. A simplistic model of a translator using a neural network may look something like the figure below: They can be used to define grammar for a translator. Neural network learns to solve problems by looking a vast amount of examples. Does the sentence make sense in the context? If these two components are not considered, the translator’s output will not be valid language. Semantics refers to the meaning of a sentence. The second thing to consider is semantics. Syntax is the basic structure of a sentence. In order to incorporate grammar, many things need to be considered. Trying to capture all of these rules, exceptions and the exceptions to the exceptions in a program rapidly deteriorates the quality of translations. To add to that, if you are bi-lingual or have ever attempted to learn a foreign language, you know that that each language has exceptions to the rules of grammar. Grammar must be incorporated into a translator’s logic. If languages were only constructed by tokens and grammar did not matter, the word-for-word translation model would be acceptable and the problem of language translation would be easily implemented.Īlas, grammar is key in making sense of things (ask the grammar nazis of the interwebs). Grammar: Defines the order tokens so that they make sense.Įvery word is a token.Translate between up to 133 languages.Although this approach is simple and easy to implement, it generally does not produce correct sentences, as seen above.
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