annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: squad_v2
tags:
- question-generation
Dataset Card for GEM/squad_v2
Dataset Description
- Homepage: https://rajpurkar.github.io/SQuAD-explorer/
- Repository: https://rajpurkar.github.io/SQuAD-explorer/
- Paper: https://arxiv.org/abs/1806.03822v1
- Leaderboard: https://rajpurkar.github.io/SQuAD-explorer/
- Point of Contact: Robin Jia
Link to Main Data Card
You can find the main data card on the GEM Website.
Dataset Summary
SQuAD2.0 is a dataset that tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. F1 score is used to evaluate models on the leaderboard. In GEM, we are using this dataset for the question-generation task in which a model should generate squad-like questions from an input text.
You can load the dataset via:
import datasets
data = datasets.load_dataset('GEM/squad_v2')
The data loader can be found here.
website
paper
authors
Pranav Rajpurkar, Robin Jia and Percy Liang
Dataset Overview
Where to find the Data and its Documentation
Webpage
Download
Paper
BibTex
@inproceedings{Rajpurkar2018KnowWY,
title={Know What You Don’t Know: Unanswerable Questions for SQuAD},
author={Pranav Rajpurkar and Robin Jia and Percy Liang},
booktitle={ACL},
year={2018}
}
Contact Name
Robin Jia
Contact Email
Has a Leaderboard?
yes
Leaderboard Link
Leaderboard Details
SQuAD2.0 tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. F1 score is used to evaluate models on the leaderboard.
Languages and Intended Use
Multilingual?
no
Covered Languages
English
License
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
Intended Use
The idea behind SQuAD2.0 dataset is to make the models understand when a question cannot be answered given a context. This will help in building models such that they know what they don't know, and therefore make the models understand language at a deeper level. The tasks that can be supported by the dataset are machine reading comprehension, extractive QA, and question generation.
Primary Task
Question Generation
Communicative Goal
Given an input passage and an answer span, the goal is to generate a question that asks for the answer.
Credit
Curation Organization Type(s)
academic
Curation Organization(s)
Stanford University
Dataset Creators
Pranav Rajpurkar, Robin Jia and Percy Liang
Funding
Facebook and NSF Graduate Research Fellowship under Grant No. DGE-114747
Who added the Dataset to GEM?
(Abinaya Mahendiran)[https://github.com/AbinayaM02], Manager Data Science, NEXT Labs,
Dataset Structure
Data Fields
The data fields are the same among all splits.
squad_v2
id: astringfeature.gem_id: astringfeature.title: astringfeature.context: astringfeature.question: astringfeature.answers: a dictionary feature containing:text: astringfeature.answer_start: aint32feature.
Example Instance
Here is an example of a validation data point. This example was too long and was cropped:
{
"gem_id": "gem-squad_v2-validation-1",
"id": "56ddde6b9a695914005b9629",
"answers": {
"answer_start": [94, 87, 94, 94],
"text": ["10th and 11th centuries", "in the 10th and 11th centuries", "10th and 11th centuries", "10th and 11th centuries"]
},
"context": "\"The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave thei...",
"question": "When were the Normans in Normandy?",
"title": "Normans"
}
Data Splits
The original SQuAD2.0 dataset has only training and dev (validation) splits. The train split is further divided into test split and added as part of the GEM datasets.
| name | train | validation | test |
|---|---|---|---|
| squad_v2 | 90403 | 11873 | 39916 |
Dataset in GEM
Rationale for Inclusion in GEM
Why is the Dataset in GEM?
SQuAD2.0 will encourage the development of new reading comprehension models that know what they don’t know, and therefore understand language at a deeper level. It can also help in building better models for answer-aware question generation .
Similar Datasets
no
Unique Language Coverage
yes
Ability that the Dataset measures
Reasoning capability
GEM-Specific Curation
Modificatied for GEM?
yes
GEM Modifications
other
Additional Splits?
yes
Split Information
The train(80%) and validation(10%) split of SQuAD2.0 are made available to public whereas the test(10%) split is not available.
As part of GEM, the train split, 80% of the original data is split into two train split (90%) and test split (remaining 10%). The idea is to provide all three splits for the users to use.
Getting Started with the Task
Previous Results
Previous Results
Measured Model Abilities
Extractive QA, Question Generation
Metrics
Other: Other Metrics, METEOR, ROUGE, BLEU
Other Metrics
- Extractive QA uses Exact Match and F1 Score
- Question generation users METEOR, ROUGE-L, BLEU-4
Previous results available?
yes
Other Evaluation Approaches
Question generation users METEOR, ROUGE-L, BLEU-4
Relevant Previous Results
@article{Dong2019UnifiedLM, title={Unified Language Model Pre-training for Natural Language Understanding and Generation}, author={Li Dong and Nan Yang and Wenhui Wang and Furu Wei and Xiaodong Liu and Yu Wang and Jianfeng Gao and M. Zhou and Hsiao-Wuen Hon}, journal={ArXiv}, year={2019}, volume={abs/1905.03197} }
Dataset Curation
Original Curation
Original Curation Rationale
The dataset is curated in three stages:
- Curating passages,
- Crowdsourcing question-answers on those passages,
- Obtaining additional answers As part of SQuAD1.1, 10000 high-quality articles from English Wikipedia is extracted using Project Nayuki’s Wikipedia’s internal PageRanks, from which 536 articles are sampled uniformly at random. From each of these articles, individual paragraphs are extracted, stripping away images, figures, tables, and discarding paragraphs shorter than 500 characters.
SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones.
Communicative Goal
To build systems that not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
Sourced from Different Sources
yes
Source Details
Wikipedia
Language Data
How was Language Data Obtained?
Found
Where was it found?
Single website
Topics Covered
The dataset contains 536 articles covering a wide range of topics, from musical celebrities to abstract concepts.
Data Validation
validated by crowdworker
Data Preprocessing
From the sampled articles from Wikipedia, individual paragraphs are extracted, stripping away images, figures, tables, and discarding paragraphs shorter than 500 characters and partitioned into training(80%), development set(10%) and test set(10%).
Was Data Filtered?
algorithmically
Filter Criteria
To retrieve high-quality articles, Project Nayuki’s Wikipedia’s internal PageRanks was used to obtain the top 10000 articles of English Wikipedia, from which 536 articles are sampled uniformly at random.
Structured Annotations
Additional Annotations?
crowd-sourced
Number of Raters
unknown
Rater Qualifications
Crowdworkers from the United States or Canada with a 97% HIT acceptance rate, a minimum of 1000 HITs, were employed to create questions.
Raters per Training Example
0
Raters per Test Example
0
Annotation Service?
yes
Which Annotation Service
other, Amazon Mechanical Turk
Annotation Values
For SQuAD 1.1 , crowdworkers were tasked with asking and answering up to 5 questions on the content of that paragraph. The questions had to be entered in a text field, and the answers had to be highlighted in the paragraph.
For SQuAD2.0, each task consisted of an entire article from SQuAD 1.1. For each paragraph in the article, workers were asked to pose up to five questions that were impossible to answer based on the paragraph alone, while referencing entities in the paragraph and ensuring that a plausible answer is present.
Any Quality Control?
validated by another rater
Quality Control Details
Questions from workers who wrote 25 or fewer questions on an article is removed; this filter helped remove noise from workers who had trouble understanding the task, and therefore quit before completing the whole article. This filter to both SQuAD2.0 and the existing answerable questions from SQuAD 1.1.
Consent
Any Consent Policy?
no
Private Identifying Information (PII)
Contains PII?
unlikely
Any PII Identification?
no identification
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?
yes