  Despite many recent advancements in language modeling, state-of-the-art
language models lack grounding in the real world and struggle with tasks
involving complex reasoning. Meanwhile, advances in the symbolic reasoning
capabilities of AI have led to systems that outperform humans in games like
chess and Go (Silver et al., 2018). Chess commentary provides an interesting
domain for bridging these two fields of research, as it requires reasoning over
a complex board state and providing analyses in natural language. In this work
we demonstrate how to combine symbolic reasoning engines with controllable
language models to generate chess commentaries. We conduct experiments to
demonstrate that our approach generates commentaries that are preferred by
human judges over previous baselines.
