LSTMs are a special kind of recurrent neural network (RNN), capable of learning long-term dependencies. Here is where Long Short-Term Memory networks (LSTMs) come into play, helping us to work with sequences whose length we can’t know a priori. Deep neural networks can achieve excellent results in very complicated tasks (speech/visual object recognition), but despite their flexibility, they can be applied only for tasks where the input and target have fixed dimensionality. Let’s try to investigate what hides in the “black boxes” that we call machine translators. Modern machine translation systems use a different approach: they allocate the rules from text by analyzing a huge set of documents.Ĭreating your own simple machine translator would be a great project for any data science resume. When we try to capture all these rules, exceptions and exceptions to the exceptions in the program, the quality of translation breaks down. If you have ever tried learning a foreign language, you know that there are always a lot of exceptions to rules. The best idea can be to teach the computer sets of grammar rules and translate the sentences according to them.
If the Google Translate engine tried to kept the translations for even short sentences, it wouldn’t work because of the huge number of possible variations. But most people don’t actually care how the engine of machine learning translation works. Now, we don’t need to struggle so much– we can translate phrases, sentences, and even large texts just by putting them in Google Translate.
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