  Automatic construction of relevant Knowledge Bases (KBs) from text, and
generation of semantically meaningful text from KBs are both long-standing
goals in Machine Learning. In this paper, we present ReGen, a bidirectional
generation of text and graph leveraging Reinforcement Learning (RL) to improve
performance. Graph linearization enables us to re-frame both tasks as a
sequence to sequence generation problem regardless of the generative direction,
which in turn allows the use of Reinforcement Learning for sequence training
where the model itself is employed as its own critic leading to Self-Critical
Sequence Training (SCST). We present an extensive investigation demonstrating
that the use of RL via SCST benefits graph and text generation on WebNLG+ 2020
and 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.
