Datasets:
Tasks:
Multiple Choice
Sub-tasks:
multiple-choice-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
albertvillanova
HF staff
Convert dataset sizes from base 2 to base 10 in the dataset card (#2)
186c1dc
metadata
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
pretty_name: Eraser MultiRC (Multi-Sentence Reading Comprehension)
dataset_info:
features:
- name: passage
dtype: string
- name: query_and_answer
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: evidences
sequence: string
splits:
- name: test
num_bytes: 9194475
num_examples: 4848
- name: train
num_bytes: 47922877
num_examples: 24029
- name: validation
num_bytes: 6529020
num_examples: 3214
download_size: 1667550
dataset_size: 63646372
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: http://cogcomp.org/multirc/
- Repository: https://github.com/CogComp/multirc
- Paper: Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 1.67 MB
- Size of the generated dataset: 63.65 MB
- Total amount of disk used: 65.32 MB
Dataset Summary
MultiRC (Multi-Sentence Reading Comprehension) is a dataset of short paragraphs and multi-sentence questions that can be answered from the content of the paragraph.
We have designed the dataset with three key challenges in mind:
- The number of correct answer-options for each question is not pre-specified. This removes the over-reliance of current approaches on answer-options and forces them to decide on the correctness of each candidate answer independently of others. In other words, unlike previous work, the task here is not to simply identify the best answer-option, but to evaluate the correctness of each answer-option individually.
- The correct answer(s) is not required to be a span in the text.
- The paragraphs in our dataset have diverse provenance by being extracted from 7 different domains such as news, fiction, historical text etc., and hence are expected to be more diverse in their contents as compared to single-domain datasets.
The goal of this dataset is to encourage the research community to explore approaches that can do more than sophisticated lexical-level matching.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 1.67 MB
- Size of the generated dataset: 63.65 MB
- Total amount of disk used: 65.32 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: astringfeature.query_and_answer: astringfeature.label: a classification label, with possible values includingFalse(0),True(1).evidences: alistofstringfeatures.
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
https://github.com/CogComp/multirc/blob/master/LICENSE
Research and Academic Use License Cognitive Computation Group University of Illinois at Urbana-Champaign
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No license is granted herein that would permit Licensee to incorporate the Software into a commercial product, or to otherwise commercially exploit the Software. Should Licensee wish to make commercial use of the Software, Licensee should contact the University, c/o the Office of Technology Management ("OTM") to negotiate an appropriate license for such commercial use. To contact the OTM: otmmailaccount@ad.uiuc.edu; telephone: (217)333-3781; fax: (217) 265-5530.
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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 = {Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL)},
year = {2018}
}
Contributions
Thanks to @lewtun, @patrickvonplaten, @thomwolf for adding this dataset.