  Conditional text generation has been a challenging task that is yet to see
human-level performance from state-of-the-art models. In this work, we
specifically focus on the Commongen benchmark, wherein the aim is to generate a
plausible sentence for a given set of input concepts. Despite advances in other
tasks, large pre-trained language models that are fine-tuned on this dataset
often produce sentences that are syntactically correct but qualitatively
deviate from a human understanding of common sense. Furthermore, generated
sequences are unable to fulfill such lexical requirements as matching
part-of-speech and full concept coverage. In this paper, we explore how
commonsense knowledge graphs can enhance model performance, with respect to
commonsense reasoning and lexically-constrained decoding. We propose strategies
for enhancing the semantic correctness of the generated text, which we
accomplish through: extracting commonsense relations from Conceptnet, injecting
these relations into the Unified Language Model (UniLM) through attention
mechanisms, and enforcing the aforementioned lexical requirements through
output constraints. By performing several ablations, we find that commonsense
injection enables the generation of sentences that are more aligned with human
understanding, while remaining compliant with lexical requirements.
