  Despite being virtually ubiquitous, sequence-to-sequence models are
challenged by their lack of diversity and inability to be externally
controlled. In this paper, we speculate that a fundamental shortcoming of
sequence generation models is that the decoding is done strictly from
left-to-right, meaning that outputs values generated earlier have a profound
effect on those generated later. To address this issue, we propose a novel
middle-out decoder architecture that begins from an initial middle-word and
simultaneously expands the sequence in both directions. To facilitate
information flow and maintain consistent decoding, we introduce a dual
self-attention mechanism that allows us to model complex dependencies between
the outputs. We illustrate the performance of our model on the task of video
captioning, as well as a synthetic sequence de-noising task. Our middle-out
decoder achieves significant improvements on de-noising and competitive
performance in the task of video captioning, while quantifiably improving the
caption diversity. Furthermore, we perform a qualitative analysis that
demonstrates our ability to effectively control the generation process of our
decoder.
