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Text infilling is defined as a task for filling in the missing part of asentence or paragraph, which is suitable for many real-world natural languagegeneration scenarios. However, given a well-trained sequential generativemodel, generating missing symbols conditioned on the context is challenging forexisting greedy approximate inference algorithms. In this paper, we propose aniterative inference algorithm based on gradient search, which is the firstinference algorithm that can be broadly applied to any neural sequencegenerative models for text infilling tasks. We compare the proposed method withstrong baselines on three text infilling tasks with various mask ratios anddifferent mask strategies. The results show that our proposed method iseffective and efficient for fill-in-the-blank tasks, consistently outperformingall baselines.

TIGS: An Inference Algorithm for Text Infilling with Gradient Search