  Text infilling is defined as a task for filling in the missing part of a
sentence or paragraph, which is suitable for many real-world natural language
generation scenarios. However, given a well-trained sequential generative
model, generating missing symbols conditioned on the context is challenging for
existing greedy approximate inference algorithms. In this paper, we propose an
iterative inference algorithm based on gradient search, which is the first
inference algorithm that can be broadly applied to any neural sequence
generative models for text infilling tasks. We compare the proposed method with
strong baselines on three text infilling tasks with various mask ratios and
different mask strategies. The results show that our proposed method is
effective and efficient for fill-in-the-blank tasks, consistently outperforming
all baselines.
