What is Seq2Seq model used for?

A Seq2Seq model is a model that takes a sequence of items (words, letters, time series, etc) and outputs another sequence of items. In the case of Neural Machine Translation, the input is a series of words, and the output is the translated series of words.

What is RNN encoder?

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols.

What does an autoencoder do?

Autoencoder is a type of neural network that can be used to learn a compressed representation of raw data. An autoencoder is composed of an encoder and a decoder sub-models. The encoder compresses the input and the decoder attempts to recreate the input from the compressed version provided by the encoder.

What is the difference between autoencoder and encoder decoder?

The autoencoder consists of two parts, an encoder, and a decoder. The encoder compresses the data from a higher-dimensional space to a lower-dimensional space (also called the latent space), while the decoder does the opposite i.e., convert the latent space back to higher-dimensional space.

Is BERT Seq2Seq?

Putting it all together. As you can see, the Seq2Seq model is a combination of the BERT encoder and TransformerXL decoder. This means we can reuse the encoder and decoder from the Seq2Seq model to train on the BERT and TransformerXL tasks. The only thing that changes, is the input and target.

What is Seq2Seq chatbot?

Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of the most commonly researched model to implement artificial intelli-gence chatbot and has shown great progress since its introduction in 2014. However, it is still in infancy and has not been applied widely in educational chatbot development.

Why RNN is used for machine translation?

Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? It can be trained as a supervised learning problem. It is strictly more powerful than a Convolutional Neural Network (CNN).

What is RNN in neural network?

Recurrent neural networks (RNN) are a class of neural networks that are helpful in modeling sequence data. Derived from feedforward networks, RNNs exhibit similar behavior to how human brains function. Simply put: recurrent neural networks produce predictive results in sequential data that other algorithms can’t.

Why autoencoder is unsupervised?

Autoencoders are considered an unsupervised learning technique since they don’t need explicit labels to train on. But to be more precise they are self-supervised because they generate their own labels from the training data.

Is Bert an autoencoder?

Unlike the AR language model, BERT is categorized as autoencoder(AE) language model. The AE language model aims to reconstruct the original data from corrupted input. The corrupted input means we use [MASK] to replace the original token into in the pre-train phase.

Which is better Gan or VAE?

By rigorous definition, VAE models explicitly learn likelihood distribution P(X|Y) through loss function. GAN does not explicitly learn likelihood distribution. But GAN generators serve to generate images that could fool the discriminator.