Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Size:
1K<n<10K
License:
metadata
pretty_name: MovieRationales
languages:
- en
paperswithcode_id: null
Dataset Card for "movie_rationales"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: http://www.cs.jhu.edu/~ozaidan/rationales/
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 3.72 MB
- Size of the generated dataset: 8.33 MB
- Total amount of disk used: 12.04 MB
Dataset Summary
The movie rationale dataset contains human annotated rationales for movie reviews.
Supported Tasks and Leaderboards
Languages
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: astringfeature.label: a classification label, with possible values includingNEG(0),POS(1).evidences: alistofstringfeatures.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| default | 1600 | 200 | 199 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
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@unpublished{eraser2019,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.