  Stories generated with neural language models have shown promise in
grammatical and stylistic consistency. However, the generated stories are still
lacking in common sense reasoning, e.g., they often contain sentences deprived
of world knowledge. We propose a simple multi-task learning scheme to achieve
quantitatively better common sense reasoning in language models by leveraging
auxiliary training signals from datasets designed to provide common sense
grounding. When combined with our two-stage fine-tuning pipeline, our method
achieves improved common sense reasoning and state-of-the-art perplexity on the
Writing Prompts (Fan et al., 2018) story generation dataset.
