  Generating paragraphs of diverse contents is important in many applications.
Existing generation models produce similar contents from homogenized contexts
due to the fixed left-to-right sentence order. Our idea is permuting the
sentence orders to improve the content diversity of multi-sentence paragraph.
We propose a novel framework PermGen whose objective is to maximize the
expected log-likelihood of output paragraph distributions with respect to all
possible sentence orders. PermGen uses hierarchical positional embedding and
designs new procedures for training, decoding, and candidate ranking in the
sentence-permuted generation. Experiments on three paragraph generation
benchmarks demonstrate PermGen generates more diverse outputs with a higher
quality than existing models.
