StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding

This repo contains the dataset and code in the EMNLP'23 paper: StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding.

An example analogy between story S1 and S2.

Use StoryAnalogy

We recommend using Hugging Face's datasets to load the story analogy dataset:

from datasets import load_dataset

dataset = load_dataset("JoeyCheng/story_analogy")

Reproduce the results

We are currently actively preparing the presentation materials and will update the code shortly.

TODO-list

[x] Reframe the dataset with huggingface datasets and present a dataset card.

[ ] Organize & release code for the experiments.

Misc

If you have any questions related to the code or the paper, please feel free to email us at jchengaj@cse.ust.hk.

If you use this research, please cite us:

@inproceedings{jiayang2023storyanalogy,
  title={StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding},
  author={Jiayang, Cheng and Qiu, Lin and Chan, Tsz and Fang, Tianqing and Wang, Weiqi and Chan, Chunkit and Ru, Dongyu and Guo, Qipeng and Zhang, Hongming and Song, Yangqiu and others},
  booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
  pages={11518--11537},
  year={2023}
}