  The task of inserting text into a specified position in a passage, known as
fill in the blank (FitB), is useful for a variety of applications where writers
interact with a natural language generation (NLG) system to craft text. While
previous work has tackled this problem with models trained specifically to do
the fill-in-the-blank task, a more useful model is one that can effectively
perform _both_ FitB and continuation. In this work, we evaluate the feasibility
of using a single model to do both tasks. We show that models pre-trained with
a FitB-style objective are capable of both tasks, while models pre-trained for
continuation are not. Finally, we show how FitB models can be easily finetuned
to allow for fine-grained control over the length and word choice of the
generation.
