OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics

方法与贡献

检验自动故事评估指标的基准，涵盖与人工判断的相关性、泛化、稳健性，以及对不同连贯性错误的敏感程度。

检验评估器能否发现已知的故事缺陷，与在潜在空间中诊断生成器输出分布的方法互补。

结合主题阅读

如何评估长文本的结构 →

引用

Jian Guan, Zhexin Zhang, Zhuoer Feng, Zitao Liu, Wenbiao Ding, Xiaoxi Mao, Changjie Fan, Minlie Huang. OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics. ACL-IJCNLP 2021.

@inproceedings{guan-etal-2021-openmeva,
  title = {{OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics}},
  author = {Guan, Jian and Zhang, Zhexin and Feng, Zhuoer and Liu, Zitao and Ding, Wenbiao and Mao, Xiaoxi and Fan, Changjie and Huang, Minlie},
  year = {2021},
  booktitle = {ACL-IJCNLP 2021},
  url = {https://aclanthology.org/2021.acl-long.500/},
  doi = {10.18653/v1/2021.acl-long.500}
}