# StoryDB: Broad Multi-language Narrative Dataset

arXiv: https://arxiv.org/abs/2109.14396 · HTML: https://mlsanity.com/paper/2109.14396/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019)
- [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020)
- [Star-Transformer](https://mlsanity.com/paper/1902.09113/) (2019)
- [Memory Transformer](https://mlsanity.com/paper/2006.11527/) (2020)
- [A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation](https://mlsanity.com/paper/1808.06945/) (2018)
- [Story Realization: Expanding Plot Events into Sentences](https://mlsanity.com/paper/1909.03480/) (2019)
- [MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants](https://mlsanity.com/paper/1905.09531/) (2019)
- [Cue Me In: Content-Inducing Approaches to Interactive Story Generation](https://mlsanity.com/paper/2010.09935/) (2020)
- [MPST: A Corpus of Movie Plot Synopses with Tags](https://mlsanity.com/paper/1802.07858/) (2018)
- [InScript: Narrative texts annotated with script information](https://mlsanity.com/paper/1703.05260/) (2017)
- [Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity](https://mlsanity.com/paper/2007.06290/) (2020)
- [DYPLODOC: Dynamic Plots for Document Classification](https://mlsanity.com/paper/2107.12226/) (2021)
- [Detecting Everyday Scenarios in Narrative Texts](https://mlsanity.com/paper/1906.04102/) (2019)

## Built on by (most cited first, 6 of 6)

- [PerCul: A Story-Driven Cultural Evaluation of LLMs in Persian](https://mlsanity.com/paper/2502.07459/) (2025)
- [What is Wrong with Language Models that Can Not Tell a Story?](https://mlsanity.com/paper/2211.05044/) (2022)
- [Multilingual Synopses of Movie Narratives: A Dataset for Vision-Language Story Understanding](https://mlsanity.com/paper/2406.13092/) (2024)
- [On the Importance and Evaluation of Narrativity in Natural Language AI Explanations](https://mlsanity.com/paper/2604.18311/) (2026)
- [FineWeb-CLaR: Culture, Language, and Region Annotations for Benchmark-Aligned Corpus Auditing](https://mlsanity.com/paper/2609.25298/) (2026)
- [CLARA: Can AI Assess Developmental Appropriateness in Children's Stories?](https://mlsanity.com/paper/2610.05783/) (2026)

## Abstract (arXiv)

This paper presents StoryDB - a broad multi-language dataset of narratives. StoryDB is a corpus of texts that includes stories in 42 different languages. Every language includes 500+ stories. Some of the languages include more than 20 000 stories. Every story is indexed across languages and labeled with tags such as a genre or a topic. The corpus shows rich topical and language variation and can serve as a resource for the study of the role of narrative in natural language processing across various languages including low resource ones. We also demonstrate how the dataset could be used to benchmark three modern multilanguage models, namely, mDistillBERT, mBERT, and XLM-RoBERTa.