Contents
What do you mean by encoder and decoder?
What is Encoder and Decoder ? The encoder is a device or a transducer or a circuit. The encoder will convert the information from one format to another format i.e like electrical signals to counters or a PLC. The decoder is a circuit used to change the code into a set of signals.
Is Seq2Seq a LSTM?
Natural Language Processing has many interesting applications and Sequence to Sequence modelling is one of those interesting applications. It has major applications in question-answering systems and language translation systems. This Seq2Seq modelling is performed by the LSTM encoder and decoder.
What is the basic sequence of every learning process?
There are four basic sequence learning problems: sequence prediction, sequence generation, sequence recognition, and sequential decision making. These “problems” show how sequences are formulated. They show the patterns sequences follow and how these different sequence learning problems are related to each other.
How does a sequence to sequence decoder work?
That is, the decoder is given an input word that it should have predicted, irrespective of what it actually predicts. For the very 1st timestep, the decoder is given start-of-sequence (SOS). The decoder is expected to end the sentence with an end-of-sequence (EOS) token.
How many input sequences are used in seq2seq model?
A total of 10,000 of the nearly 150,000 examples in the data file are used in the dataset. Some technical details of the prepared data are as follows: Input Sequences: Padded to a maximum length of 16 characters with a vocabulary of 71 different characters (10000, 16, 71).
What are the parts of a sequence to sequence model?
The model consists of 3 parts: encoder, intermediate (encoder) vector and decoder. A stack of several recurrent units (LSTM or GRU cells for better performance) where each accepts a single element of the input sequence, collects information for that element and propagates it forward.
Why are there enhancements to the sequence model?
The reason is that using a single vector for encoding the whole input sequence is not capable of capturing the whole information. This is why multiple enhancements are being introduced. Each one aims to strengthen the performance of this model on slightly complex tasks with long input and output sequences.