  The encoder-decoder framework has achieved promising process for many
sequence generation tasks, such as neural machine translation and text
summarization. Such a framework usually generates a sequence token by token
from left to right, hence (1) this autoregressive decoding procedure is
time-consuming when the output sentence becomes longer, and (2) it lacks the
guidance of future context which is crucial to avoid under translation. To
alleviate these issues, we propose a synchronous bidirectional sequence
generation (SBSG) model which predicts its outputs from both sides to the
middle simultaneously. In the SBSG model, we enable the left-to-right (L2R) and
right-to-left (R2L) generation to help and interact with each other by
leveraging interactive bidirectional attention network. Experiments on neural
machine translation (En-De, Ch-En, and En-Ro) and text summarization tasks show
that the proposed model significantly speeds up decoding while improving the
generation quality compared to the autoregressive Transformer.
