The seq2seq (sequence to sequence) model is a type of encoder-decoder deep learning model commonly employed in natural language processing that uses recurrent neural networks like LSTM to generate output. seq2seq can generate output token by token or character by character. In machine translation, seq2seq networks have an encoder accepting language as input and outputting state vectors and a decoder accepting the encoder’s final state and outputting possible translations.