  We introduce the Scratchpad Mechanism, a novel addition to the
sequence-to-sequence (seq2seq) neural network architecture and demonstrate its
effectiveness in improving the overall fluency of seq2seq models for natural
language generation tasks. By enabling the decoder at each time step to write
to all of the encoder output layers, Scratchpad can employ the encoder as a
"scratchpad" memory to keep track of what has been generated so far and thereby
guide future generation. We evaluate Scratchpad in the context of three
well-studied natural language generation tasks --- Machine Translation,
Question Generation, and Text Summarization --- and obtain state-of-the-art or
comparable performance on standard datasets for each task. Qualitative
assessments in the form of human judgements (question generation), attention
visualization (MT), and sample output (summarization) provide further evidence
of the ability of Scratchpad to generate fluent and expressive output.
