Machine Translation Models

Sequence to Sequence

Introduced by Sutskever et al. in Sequence to Sequence Learning with Neural Networks

Seq2Seq, or Sequence To Sequence, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one LSTM, the encoder, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the decoder, to extract the output sequence from that vector. The second LSTM is essentially a recurrent neural network language model except that it is conditioned on the input sequence.

(Note that this page refers to the original seq2seq not general sequence-to-sequence models)

Source: Sequence to Sequence Learning with Neural Networks


Paper Code Results Date Stars



Component Type
Recurrent Neural Networks