# TIGS: An Inference Algorithm for Text Infilling with Gradient Search

## TL;DR

This paper introduces TIGS, a gradient search-based inference algorithm for text infilling that effectively improves upon existing methods across various tasks and mask strategies.

## Contribution

The paper presents the first broadly applicable gradient search inference algorithm for neural sequence models in text infilling tasks.

## Key findings

- Outperforms baseline methods on multiple text infilling benchmarks.
- Effective across various mask ratios and strategies.
- Demonstrates efficiency in fill-in-the-blank tasks.

## Abstract

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.