The Case for a Single Model that can Both Generate Continuations and Fill in the Blank

Abstract

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-theblank 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 FITBstyle 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.