POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training
Yizhe Zhang, Guoyin Wang, Chunyuan Li, Zhe Gan, Chris Brockett, Bill Dolan · 2020
Large-scale pre-trained language models, such as BERT and GPT-2, have achieved excellent performance in language representation learning and free-form text generation.However, these models cannot be directly employed to generate text under specified lexical constraints.To address this challenge, we present POINTER 1 , a simple yet novel insertion-based approach for hard-constrained text generation.The proposed method operates by progressively inserting new tokens between existing tokens in a parallel manner.This procedure is recursively applied until a sequence is completed.The resulting coarse-to-fine hierarchy makes the generation process intuitive and interpretable.We pre-train our model with the proposed progressive insertion-based objective on a 12GB Wikipedia dataset, and finetune it on downstream hard-constrained generation tasks.Non-autoregressive decoding yields an empirically logarithmic time complexity during inference time.Experimental results on both News and Yelp datasets demonstrate that POINTER achieves state-of-the-art performance on constrained text generation.We released the pre-trained models and the source code to facilitate future research 2 .