How are encoder-decoder models used in seq2seq problems?

How are encoder-decoder models used in seq2seq problems?

Sequence-to-Sequence (Seq2Seq) problems is a special class of Sequence Modelling Problems in which both, the input and the output is a sequence. Encoder-Decoder models were originally built to solve such Seq2Seq problems.

What kind of transformers are used in seq2seq?

The Seq2SeqModel class is used for Sequence-to-Sequence tasks. Currently, four main types of Sequence-to-Sequence models are available. The decoder must be a bert model. The encoder can be one of [bert, roberta, distilbert, camembert, electra]. The encoder and the decoder must be of the same “size”.

How does seq2seq model work with attention?

With attention, Seq2seq does not forget the source input. With attention, the decoder knows where to focus. The context vector turned out to be a bottleneck for these types of models. It made it challenging for the models to deal with long sentences.

How many languages can seq2seq model be trained in?

The list of supported language pairs can be found here. The 1,000+ models were originally trained by Jörg Tiedemann using the Marian C++ library, which supports fast training and translation.

How is the training process in seq2seq?

The training process in Seq2seq models is started with converting each pair of sentences into Tensors from their Lang index. Our sequence to sequence model will use SGD as the optimizer and NLLLoss function to calculate the losses. The training process begins with feeding the pair of a sentence to the model to predict the correct output.

Why is encoder-decoder sequence to sequence model not good?

The above explanation just covers the simplest sequence to sequence model and, thus, we cannot expect it to perform well on complex tasks. 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.

How is seq2seq used in machine translation?

Seq2Seq is a method of encoder-decoder based machine translation that maps an input of sequence to an output of sequence with a tag and attention value. The idea is to use 2 RNN that will work together with a special token and trying to predict the next state sequence from the previous sequence.