• Description:

The movie rationale dataset contains human annotated rationales for movie reviews.

Split Examples
'test' 199
'train' 1,600
'validation' 200
  • Feature structure:
    'evidences': Sequence(Text(shape=(), dtype=string)),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
    'review': Text(shape=(), dtype=string),
  • Feature documentation:
Feature Class Shape Dtype Description
evidences Sequence(Text) (None,) string
label ClassLabel int64
review Text string
  • Citation:
    title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
    author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
  author    =  {Omar F. Zaidan  and  Jason Eisner  and  Christine Piatko},
  title     =  {Machine Learning with Annotator Rationales to Reduce Annotation Cost},
  booktitle =  {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning},
  month     =  {December},
  year      =  {2008}