Seq2Seq

2000년 도입 · 논문 700편에서 사용

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)

출처: Sequence to Sequence Learning with Neural Networks

소개 논문: Sequence to Sequence Learning with Neural Networks