TIGS: An Inference Algorithm for Text Infilling with Gradient Search

This repo contains the code and data of the following paper:

TIGS: An Inference Algorithm for Text Infilling with Gradient Search, Dayiheng Liu, Jie Fu, Pengfei Liu, Jiancheng Lv, Association for Computational Linguistics. ACL 2019 [arXiv]

Overview

Given a well-trained sequential generative model, generating missing symbols conditioned on the context is challenging for existing greedy approximate inference algorithms. We propose a dramatically different inference approach called Text Infilling with Gradient Search (TIGS), in which we search for infilled words based on gradient information to fill in the blanks. To the best of our knowledge, this could be the first inference algorithm that does not require any modification or training of the model and can be broadly used in any sequence generative model to solve the fillin-the-blank tasks.

Dependencies

• Jupyter notebook 4.4.0
• Python 3.6
• Tensorflow 1.6.0+

Quick Start

• Training: Run TIGS_train.ipynb
• Inference: Run TIGS_inference.ipynb

Trained Model

Download the trained models at the link https://drive.google.com/open?id=1IABzc6ovkR6Uprnl3isSAWf6ax2fLHgH

• The APRC trained model can be found in Model/APRC
• The Poem trained model can be found in Model/Poem
• The Daily trained model can be found in Model/Daily

Dataset

Download the datasets at the link https://drive.google.com/open?id=1GKyBtU0pPysB10wdsqMxYDoQ5CRQIXI8

• The APRC dataset can be found in Data/APRC
• The Poem dataset can be found in Data/Poem
• The Daily dataset can be found in Data/Daily