“While generating the 10th word of the French sentence it looks at the first nine words in the English sentence.” The Recurrent Neural Network is only looking a the past words, and not the words that come after the current word. Recurrent Neural Networks use past information to generate the present information. This is due to the lack of complexity in this method.
#Itranslate computer code
The Code Emporium video tested out the LSTM-RNN Encoder method on longer sentences, and found that the translations did not work as well.
Convert vector to French sentence (using another recurrent neural network)Īccording to research from a 2014 paper on Neural Machine Translation, the Encoder-Decoder Architecture model pictured above works best for medium length sentences with 15-20 words (Cho et al). Take English sentence and convert into computer language (a vector) using a recurrent neural network For this particular task, a Recurrent Neural Network will be used (neural network specifically for sentences). So, the first step will always be to convert the language into computer language. They process information using numbers (vectors). In the case of Google Translate, the neural networks job or problem to solve, is to take an English sentence ( input) and turn it into a French translation ( output).Īs we learned from the data structures module, computers do not process information the way our brains do. Looking at how Google Translate’s Neural Network works, the Code Emportium video describes a neural network as a problem solver.
The PBS video also explains this is the way that Siri is able to deconstruct simple word commands. Additionally, speech recognition apps with the best accuracy use deep neural networks. This helps computers process information more easily and accurately.Parse Trees: link every word with a likely part of speech+ show sentence construction Using phrase structures, computers are able to construct parse trees Development of Phrase Structure Rules which encapsulate the grammar of a language.In order for computers to deconstruct sentences, grammar is necessary.The PBS Crash Course video breaks down how computers use NLP methods.ĭeconstructing sentences into smaller pieces that could be easily processed: Because computers do not understand grammar, they need a process in which they can deconstruct sentences and reconstruct them in another language in a way that makes sense. Words can have several different meanings and also depend on their structure within a sentence to make sense. Natural Language Processing addresses this problem of complexity and ambiguity in language translation. This “context’ in language is known as grammar. As seen in the readings and videos for this module, translating a text in another language needs more context than a dictionary can provide. Language translation is more complex than a simple word-to-word replacement method.
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