# Improving Neural Story Generation by Targeted Common Sense Grounding

arXiv: https://arxiv.org/abs/1908.09451 · HTML: https://mlsanity.com/paper/1908.09451/

## Key facts

- Cited by 16 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 16 of 16)

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- [The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation](https://mlsanity.com/paper/2109.06835/) (2021)
- [Transformer-based Conditional Variational Autoencoder for Controllable Story Generation](https://mlsanity.com/paper/2101.00828/) (2021)
- [Automatic Story Generation: Challenges and Attempts](https://mlsanity.com/paper/2102.12634/) (2021)
- [Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations](https://mlsanity.com/paper/2106.13876/) (2021)
- [Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation](https://mlsanity.com/paper/2204.10703/) (2022)
- [GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence](https://mlsanity.com/paper/2310.05388/) (2023)
- [On-the-Fly Attention Modulation for Neural Generation](https://mlsanity.com/paper/2101.00371/) (2021)
- [Sketch and Customize: A Counterfactual Story Generator](https://mlsanity.com/paper/2104.00929/) (2021)
- [Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events](https://mlsanity.com/paper/2101.00822/) (2021)
- [TopNet: Learning from Neural Topic Model to Generate Long Stories](https://mlsanity.com/paper/2112.07259/) (2021)
- [An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters](https://mlsanity.com/paper/2209.00200/) (2022)
- [CASE: Commonsense-Augmented Score with an Expanded Answer Space](https://mlsanity.com/paper/2311.01684/) (2023)

## Abstract (arXiv)

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.