UniCausal

UniCausal is a unified benchmark that harmonises twelve existing causal NLP datasets into a single repository with consistent annotation schema, enabling cross-dataset training and fair multi-corpus evaluation.

Overview

Domain	Multiple
Year	2023
Sentences	14,903

Tasks & Splits

Task	Train	Test
Causality Detection	—	—
Causal Event Candidate Extraction	—	—
Causality Identification	—	—

Citation

@inproceedings{tan:2023,
  title      = {{{UniCausal}}: {{Unified Benchmark}} and {{Repository}} for {{Causal Text Mining}}},
  shorttitle = {{{UniCausal}}},
  booktitle  = {Big {{Data Analytics}} and {{Knowledge Discovery}} - 25th {{International Conference}}, {{DaWaK}} 2023, {{Penang}}, {{Malaysia}}, {{August}} 28-30, 2023, {{Proceedings}}},
  author     = {Tan, Fiona Anting and Zuo, Xinyu and Ng, See-Kiong},
  editor     = {Wrembel, Robert and Gamper, Johann and Kotsis, Gabriele and Tjoa, A. Min and Khalil, Ismail},
  year       = 2023,
  series     = {Lecture {{Notes}} in {{Computer Science}}},
  volume     = {14148},
  pages      = {248--262},
  publisher  = {Springer},
  doi        = {10.1007/978-3-031-39831-5_23},
  urldate    = {2024-09-10}
}