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Automatic construction of relevant Knowledge Bases (KBs) from text, andgeneration of semantically meaningful text from KBs are both long-standinggoals in Machine Learning. In this paper, we present ReGen, a bidirectionalgeneration of text and graph leveraging Reinforcement Learning (RL) to improveperformance. Graph linearization enables us to re-frame both tasks as asequence to sequence generation problem regardless of the generative direction,which in turn allows the use of Reinforcement Learning for sequence trainingwhere the model itself is employed as its own critic leading to Self-CriticalSequence Training (SCST). We present an extensive investigation demonstratingthat the use of RL via SCST benefits graph and text generation on WebNLG+ 2020and TekGen datasets. Our system provides state-of-the-art results on WebNLG+2020 by significantly improving upon published results from the WebNLG 2020+Challenge for both text-to-graph and graph-to-text generation tasks.

ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models