论文标题
单个型号的情况，该模型既可以生成连续又填充空白
The Case for a Single Model that can Both Generate Continuations and Fill in the Blank
论文作者
论文摘要
将文本插入段落中指定位置的任务（称为空白（FITB））对于各种应用程序与作家与自然语言生成（NLG）系统进行互动以制作文本的应用很有用。虽然以前的工作已经通过专门完成填空任务的模型解决了这个问题，但更有用的模型是可以有效地执行_both_ fitb和延续的模型。在这项工作中，我们评估了使用单个模型完成这两个任务的可行性。我们表明，通过FITB式目标进行预训练的模型都可以完成这两个任务，而预先训练的持续模型却没有。最后，我们展示了如何轻松地对FITB模型进行填充，以允许对一代的长度和单词选择进行细粒度的控制。
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.
