Reinforced generation: reinforcement learning for text and knowledge graph bi-directional generation using pretrained language models

Abstract

Obtain access to a pretrained encoder-decoder language model. Using a dataset including a plurality of text-graph pairs, carry out first fine-tuning training on the pre-trained language model by minimizing cross-entropy loss. A text portion of each text-graph pair includes a list of text tokens and a graph portion of each text-graph pair includes a list of graph tokens. The first fine-tuning training results in an intermediate model. Carry out second fine-tuning training on the intermediate model, by reinforcement learning, to obtain a final model. Make the final model available for deployment.