Abstract
The emergence of pre-trained language models has taken story generation, which is the task of automatically generating a comprehensible story from limited information, to a new stage. Although generated stories from the language models are fluent and grammatically correct, the lack of coherence affects their quality. We propose a knowledge-based multi-stage model that incorporates the schema, a kind of structured knowledge, to guide coherent story generation. Our framework includes a schema acquisition module, a plot generation module, and a surface realization module. In the schema acquisition module, high-relevant structured knowledge pieces are selected as a schema. In the plot generation module, a coherent plot plan is navigated by the schema. In the surface realization module, conditioned by the generated plot, a story is generated. Evaluations show that our methods can generate more comprehensible stories than strong baselines, especially with higher global coherence and less repetition.- Anthology ID:
- 2023.ranlp-1.74
- Volume:
- Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 681–690
- Language:
- URL:
- https://aclanthology.org/2023.ranlp-1.74
- DOI:
- Cite (ACL):
- Congda Ma, Kotaro Funakoshi, Kiyoaki Shirai, and Manabu Okumura. 2023. Coherent Story Generation with Structured Knowledge. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 681–690, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- Coherent Story Generation with Structured Knowledge (Ma et al., RANLP 2023)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2023.ranlp-1.74.pdf