Improving Conditioning in Context-Aware Sequence to Sequence Models

Understand

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.