metadata
paperswithcode_id: null
Dataset Card for "eraser_multi_rc"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://cogcomp.seas.upenn.edu/multirc/
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 1.59 MB
- Size of the generated dataset: 60.70 MB
- Total amount of disk used: 62.29 MB
Dataset Summary
Eraser Multi RC is a dataset for queries over multi-line passages, along with answers and a rationalte. Each example in this dataset has the following 5 parts
- A Mutli-line Passage
- A Query about the passage
- An Answer to the query
- A Classification as to whether the answer is right or wrong
- An Explanation justifying the classification
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: 1.59 MB
- Size of the generated dataset: 60.70 MB
- Total amount of disk used: 62.29 MB
An example of 'validation' looks as follows.
This example was too long and was cropped:
{
"evidences": "[\"Allan sat down at his desk and pulled the chair in close .\", \"Opening a side drawer , he took out a piece of paper and his ink...",
"label": 0,
"passage": "\"Allan sat down at his desk and pulled the chair in close .\\nOpening a side drawer , he took out a piece of paper and his inkpot...",
"query_and_answer": "Name few objects said to be in or on Allan 's desk || Eraser"
}
Data Fields
The data fields are the same among all splits.
default
passage
: astring
feature.query_and_answer
: astring
feature.label
: a classification label, with possible values includingFalse
(0),True
(1).evidences
: alist
ofstring
features.
Data Splits
name | train | validation | test |
---|---|---|---|
default | 24029 | 3214 | 4848 |
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@unpublished{eraser2019,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{MultiRC2018,
author = {Daniel Khashabi and Snigdha Chaturvedi and Michael Roth and Shyam Upadhyay and Dan Roth},
title = {Looking Beyond the Surface:A Challenge Set for Reading Comprehension over Multiple Sentences},
booktitle = {NAACL},
year = {2018}
}
Contributions
Thanks to @lewtun, @patrickvonplaten, @thomwolf for adding this dataset.