annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids:
- unknown
pretty_name: ART
tags:
- reasoning
Dataset Card for GEM/ART
Dataset Description
- Homepage: http://abductivecommonsense.xyz/
- Repository: https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip
- Paper: https://openreview.net/pdf?id=Byg1v1HKDB
- Leaderboard: N/A
- Point of Contact: Chandra Bhagavatulla
Link to Main Data Card
You can find the main data card on the GEM Website.
Dataset Summary
Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation. This data loader focuses on abductive NLG: a conditional English generation task for explaining given observations in natural language.
You can load the dataset via:
import datasets
data = datasets.load_dataset('GEM/ART')
The data loader can be found here.
website
paper
authors
Chandra Bhagavatula (AI2), Ronan Le Bras (AI2), Chaitanya Malaviya (AI2), Keisuke Sakaguchi (AI2), Ari Holtzman (AI2, UW), Hannah Rashkin (AI2, UW), Doug Downey (AI2), Wen-tau Yih (AI2), Yejin Choi (AI2, UW)
Dataset Overview
Where to find the Data and its Documentation
Webpage
Download
Paper
BibTex
@inproceedings{
Bhagavatula2020Abductive,
title={Abductive Commonsense Reasoning},
author={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Byg1v1HKDB}
}
Contact Name
Chandra Bhagavatulla
Contact Email
Has a Leaderboard?
no
Languages and Intended Use
Multilingual?
no
Covered Languages
English
Whose Language?
Crowdworkers on the Amazon Mechanical Turk platform based in the U.S, Canada, U.K and Australia.
License
apache-2.0: Apache License 2.0
Intended Use
To study the viability of language-based abductive reasoning. Training and evaluating models to generate a plausible hypothesis to explain two given observations.
Primary Task
Reasoning
Credit
Curation Organization Type(s)
industry
Curation Organization(s)
Allen Institute for AI
Dataset Creators
Chandra Bhagavatula (AI2), Ronan Le Bras (AI2), Chaitanya Malaviya (AI2), Keisuke Sakaguchi (AI2), Ari Holtzman (AI2, UW), Hannah Rashkin (AI2, UW), Doug Downey (AI2), Wen-tau Yih (AI2), Yejin Choi (AI2, UW)
Funding
Allen Institute for AI
Who added the Dataset to GEM?
Chandra Bhagavatula (AI2), Ronan LeBras (AI2), Aman Madaan (CMU), Nico Daheim (RWTH Aachen University)
Dataset Structure
Data Fields
observation_1: A string describing an observation / event.observation_2: A string describing an observation / event.label: A string that plausibly explains why observation_1 and observation_2 might have happened.
How were labels chosen?
Explanations were authored by crowdworkers on the Amazon Mechanical Turk platform using a custom template designed by the creators of the dataset.
Example Instance
{
'gem_id': 'GEM-ART-validation-0',
'observation_1': 'Stephen was at a party.',
'observation_2': 'He checked it but it was completely broken.',
'label': 'Stephen knocked over a vase while drunk.'
}
Data Splits
train: Consists of training instances.dev: Consists of dev instances.test: Consists of test instances.
Dataset in GEM
Rationale for Inclusion in GEM
Why is the Dataset in GEM?
Abductive reasoning is a crucial capability of humans and ART is the first dataset curated to study language-based abductive reasoning.
Similar Datasets
no
Ability that the Dataset measures
Whether models can reason abductively about a given pair of observations.
GEM-Specific Curation
Modificatied for GEM?
no
Additional Splits?
no
Getting Started with the Task
Pointers to Resources
Previous Results
Previous Results
Measured Model Abilities
Whether models can reason abductively about a given pair of observations.
Metrics
BLEU, BERT-Score, ROUGE
Previous results available?
no
Dataset Curation
Original Curation
Sourced from Different Sources
no
Language Data
How was Language Data Obtained?
Crowdsourced
Where was it crowdsourced?
Amazon Mechanical Turk
Language Producers
Language producers were English speakers in U.S., Canada, U.K and Australia.
Topics Covered
No
Data Validation
validated by crowdworker
Was Data Filtered?
algorithmically
Filter Criteria
Adversarial filtering algorithm as described in the paper
Structured Annotations
Additional Annotations?
automatically created
Annotation Service?
no
Annotation Values
Each observation is associated with a list of COMET (https://arxiv.org/abs/1906.05317) inferences.
Any Quality Control?
none
Consent
Any Consent Policy?
no
Private Identifying Information (PII)
Contains PII?
no PII
Justification for no PII
The dataset contains day-to-day events. It does not contain names, emails, addresses etc.
Maintenance
Any Maintenance Plan?
no
Broader Social Context
Previous Work on the Social Impact of the Dataset
Usage of Models based on the Data
no
Impact on Under-Served Communities
Addresses needs of underserved Communities?
no
Discussion of Biases
Any Documented Social Biases?
no
Considerations for Using the Data
PII Risks and Liability
Potential PII Risk
None
Licenses
Copyright Restrictions on the Dataset
public domain
Copyright Restrictions on the Language Data
public domain