  Neural sequence to sequence models are well established for applications
which can be cast as mapping a single input sequence into a single output
sequence. In this work, we focus on cases where generation is conditioned on
both a short query and a long context, such as abstractive question answering
or document-level translation. We modify the standard sequence-to-sequence
approach to make better use of both the query and the context by expanding the
conditioning mechanism to intertwine query and context attention. We also
introduce a simple and efficient data augmentation method for the proposed
model. Experiments on three different tasks show that both changes lead to
consistent improvements.
