movie_rationales / README.md
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Dataset Card for "movie_rationales"

Table of Contents

Dataset Description

Dataset Summary

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

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

default

  • Size of downloaded dataset files: 3.72 MB
  • Size of the generated dataset: 8.33 MB
  • Total amount of disk used: 12.04 MB

An example of 'validation' looks as follows.

{
    "evidences": ["Fun movie"],
    "label": 1,
    "review": "Fun movie\n"
}

Data Fields

The data fields are the same among all splits.

default

  • review: a string feature.
  • label: a classification label, with possible values including NEG (0), POS (1).
  • evidences: a list of string features.

Data Splits

name train validation test
default 1600 200 199

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information


@unpublished{eraser2019,
    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}
}
@InProceedings{zaidan-eisner-piatko-2008:nips,
  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}
}

Contributions

Thanks to @thomwolf, @patrickvonplaten, @lewtun for adding this dataset.