  Neural sequence generation is typically performed token-by-token and
left-to-right. Whenever a token is generated only previously produced tokens
are taken into consideration. In contrast, for problems such as sequence
classification, bidirectional attention, which takes both past and future
tokens into consideration, has been shown to perform much better. We propose to
make the sequence generation process bidirectional by employing special
placeholder tokens. Treated as a node in a fully connected graph, a placeholder
token can take past and future tokens into consideration when generating the
actual output token. We verify the effectiveness of our approach experimentally
on two conversational tasks where the proposed bidirectional model outperforms
competitive baselines by a large margin.
