Gated Recurrent Unit (GRU) / RNN Encoder-Decoder

Editorial record

Plain-language summary

One recurrent network (the encoder) reads a source sentence and compresses it into a fixed-length vector, and a second recurrent network (the decoder) generates the target sentence from that vector, trained jointly to maximize the probability of the correct output. To make the recurrence trainable over longer spans the paper introduced the GRU, which uses update and reset gates to control how much past state is kept or overwritten, achieving LSTM-like behavior with a simpler structure. The encoder-decoder setup became the template for neural machine translation and sequence-to-sequence learning, and its learned phrase representations improved a statistical translation system.