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Which of the following type of RNN can be used for machine translation?
The simplest idea is to use a basic RNN such as the following diagram below. In this diagram the RNN is unrolled to make it easier to understand whats happening. This type of RNN is a sequence to sequence RNN (seq2seq).
Can RNN be used for machine translation?
The Encoder-Decoder RNN Structure The task of machine translation consists of reading text in one language and generating text in another language. When neural networks are used for this task, we talk about neural machine translation (NMT)[i] [ii].
What is translation algorithm?
The main thing about this approach is that now the Google Translate algorithm uses only one system instead of a huge set for every pair of languages. The system requires a “token” at the beginning of the input sentence which specifies the language you’re trying to translate the phrase into.
How can RNNs be used for machine translation?
Since we have a basic understanding of LSTMs and RNNs, let’s try and apply some of this into developing a machine translation model using Keras. This will demonstrate how a sequence to sequence LSTM network can be used to translate text from English to French. Data can be found here.
How are RNNs used in a neural network?
At a particular time step t, X (t) is the input to the network and h (t) is the output of the network. A is the RNN cell which contains neural networks just like a feed-forward net. This loop structure allows the neural network to take the sequence of the input.
What are the different types of NLP techniques?
On the model side, it covers word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, and convolutional neural networks, as well as some recent models involving a memory component.
Why are LSTM networks used instead of RNNs?
LSTM networks were made with one purpose in mind — to solve long term dependency problems that traditional RNNs have. It’s in their inherent nature to remember things from a long time ago, and they don’t struggle with it at all. The repeating module in an LSTM contains 4 layers, as opposed to only one in an RNN.