Hierarchical neural story generation
Informations
- Type:
- article
- Auteurs:
- Fan, Angela and Lewis, Mike and Dauphin, Yann
- Pertinence:
-
Haute
- Référence:
- akoury2020storium
- Doi:
- Mots-clés:
- Url:
- https://arxiv.org/abs/1805.04833
- Date de publication:
- 12/2017
- Résumé:
- Génération d'histoires sur base d'un prompt.
- Abstract:
- We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.
- Pdf:
- Lien pdf
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Citations
1 articles
| Titre | Type | Pertinence | Auteurs | Date Publication | Références | Citations | Actions |
|---|---|---|---|---|---|---|---|
| Wordcraft: Story Writing With Large Language Models | inproceedings | Haute | Yuan, Ann and Coenen, Andy and Reif, Emily and Ippolito, Daphne | 03/2022 | 3 | 6 |
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