  We propose a simple and effective modeling framework for controlled
generation of multiple, diverse outputs. We focus on the setting of generating
the next sentence of a story given its context. As controllable dimensions, we
consider several sentence attributes, including sentiment, length, predicates,
frames, and automatically-induced clusters. Our empirical results demonstrate:
(1) our framework is accurate in terms of generating outputs that match the
target control values; (2) our model yields increased maximum metric scores
compared to standard n-best list generation via beam search; (3) controlling
generation with semantic frames leads to a stronger combination of diversity
and quality than other control variables as measured by automatic metrics. We
also conduct a human evaluation to assess the utility of providing multiple
suggestions for creative writing, demonstrating promising results for the
potential of controllable, diverse generation in a collaborative writing
system.
